Chapter5_Pooja.ipynb

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        "<a href=\"https://colab.research.google.com/github/iupui-soic/pybootcamp/blob/main/Chapter5_Pooja.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "**Chapter 5: Support Vector Machines**\n",
        "\n"
      ],
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        "id": "P91JMFtGEnsj"
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      "cell_type": "markdown",
      "source": [
        "**RECAP: DECISION BOUNDARIES**"
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        "id": "pgD6SjynFDM0"
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      "cell_type": "markdown",
      "source": [
        "Previously, we had used 'Iris' dataset to study logistic regression. The iris dataset contains the sepal and petal length and width (all in cm) of 150 iris flowers which belong to 3 different classes : Iris Virginica, Iris Setosa and Iris Versicolor.We then tried to build a classifier to detect the Iris Virgininca type based on the 'petal' width feature."
      ],
      "metadata": {
        "id": "Wua6c9fhFPT4"
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    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import load_iris\n",
        "\n",
        "iris = load_iris(as_frame=True)\n"
      ],
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        "id": "stAjZu25FCoN"
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              "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
              "0                5.1               3.5                1.4               0.2\n",
              "1                4.9               3.0                1.4               0.2\n",
              "2                4.7               3.2                1.3               0.2\n",
              "3                4.6               3.1                1.5               0.2\n",
              "4                5.0               3.6                1.4               0.2"
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              "      <th></th>\n",
              "      <th>sepal length (cm)</th>\n",
              "      <th>sepal width (cm)</th>\n",
              "      <th>petal length (cm)</th>\n",
              "      <th>petal width (cm)</th>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"iris\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"sepal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2073644135332772,\n        \"min\": 4.6,\n        \"max\": 5.1,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.9,\n          5.0,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2588435821108957,\n        \"min\": 3.0,\n        \"max\": 3.6,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.0,\n          3.6,\n          3.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal length (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07071067811865474,\n        \"min\": 1.3,\n        \"max\": 1.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.4,\n          1.3,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal width (cm)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.2,\n        \"max\": 0.2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 76
        }
      ],
      "source": [
        "iris.data.head(5) #inputs"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "iris.target.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4RsoPvCKGq_z",
        "outputId": "a9c8ee82-e9df-433e-b54a-064dc614a5bb"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "2    0\n",
              "3    0\n",
              "4    0\n",
              "Name: target, dtype: int64"
            ]
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    },
    {
      "cell_type": "code",
      "source": [
        "iris.target_names"
      ],
      "metadata": {
        "colab": {
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        "id": "sw6CoOP6HVzb",
        "outputId": "f6b9777f-5f70-454d-a751-e94f5a7fd880"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['setosa', 'versicolor', 'virginica'], dtype='<U10')"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train and test split followed by training a logistic regression model on the training set:"
      ],
      "metadata": {
        "id": "ZvkKzMQJH1q7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X = iris.data[[\"petal width (cm)\"]].values\n",
        "y = iris.target_names[iris.target] == 'virginica'\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)\n",
        "\n",
        "log_reg = LogisticRegression(random_state=42)\n",
        "log_reg.fit(X_train, y_train)\n",
        "\n",
        "\n"
      ],
      "metadata": {
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          "height": 74
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        "id": "l-4mvqBiHicb",
        "outputId": "97b462e3-d532-4ca9-97d6-0a546f56fe79"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "LogisticRegression(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" checked><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Coefficients\n",
        "b0 = log_reg.intercept_[0]\n",
        "b1 = log_reg.coef_[0][0]\n",
        "print(b0)\n",
        "print(b1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-8INNfY6oOZ-",
        "outputId": "a5e6d990-779e-4ac4-bb76-441a6f7e38d2"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-6.332930134234662\n",
            "3.836377151829212\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "X_new = np.linspace(0, 3, 1000).reshape(-1, 1)  # reshape to get a column vector\n",
        "y_proba = log_reg.predict_proba(X_new)\n",
        "decision_boundary = X_new[y_proba[:, 1] >= 0.5][0, 0]\n",
        "\n",
        "plt.figure(figsize=(8, 3))  # extra code – not needed, just formatting\n",
        "plt.plot(X_new, y_proba[:, 0], \"b--\", linewidth=2, color=\"red\",\n",
        "         label=\"Not Iris virginica proba\")\n",
        "plt.plot(X_new, y_proba[:, 1], \"g-\", linewidth=2, label=\"Iris virginica proba\")\n",
        "plt.plot([decision_boundary, decision_boundary], [0, 1], \"k:\", linewidth=2,\n",
        "         label=\"Decision boundary\")\n",
        "\n",
        "# extra code – this section beautifies and saves Figure 4–23\n",
        "plt.arrow(x=decision_boundary, y=0.08, dx=-0.3, dy=0,\n",
        "          head_width=0.05, head_length=0.1, fc=\"b\", ec=\"b\")\n",
        "plt.arrow(x=decision_boundary, y=0.92, dx=0.3, dy=0,\n",
        "          head_width=0.05, head_length=0.1, fc=\"g\", ec=\"g\")\n",
        "plt.plot(X_train[y_train == 0], y_train[y_train == 0], \"bs\")\n",
        "plt.plot(X_train[y_train == 1], y_train[y_train == 1], \"g^\")\n",
        "plt.xlabel(\"Petal width (cm)\")\n",
        "plt.ylabel(\"Probability\")\n",
        "plt.legend(loc=\"center left\")\n",
        "plt.axis([0, 3, -0.02, 1.02])\n",
        "plt.grid()\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 347
        },
        "id": "M_mo6Js6IH8D",
        "outputId": "ffb5339f-c5db-463a-d540-ac6c5cbab473"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-81-ae358ee3cb7c>:8: UserWarning: color is redundantly defined by the 'color' keyword argument and the fmt string \"b--\" (-> color='b'). The keyword argument will take precedence.\n",
            "  plt.plot(X_new, y_proba[:, 0], \"b--\", linewidth=2, color=\"red\",\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The petal width of Iris virginica flowers (represented as triangles) ranges from 1.4\n",
        "cm to 2.5 cm,  while the other iris flowers (represented by squares) generally have a smaller petal width, ranging from 0.1 cm to 1.8 cm."
      ],
      "metadata": {
        "id": "6p4KqyYiIszJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Above about 2 cm the classifier is highly confident that the flower is an Iris\n",
        "virginica, while below 1 cm it is highly confident that it is not an Iris virginica (in both cases, a high probability is estimated). In between these extremes, the classifier is not unsure about which class the instance would belong to."
      ],
      "metadata": {
        "id": "J625StMAIsd0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "decision_boundary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fXEw-zA7LUmq",
        "outputId": "69c7cadd-16ef-47de-80a2-86c2c91faddf"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.6516516516516517"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.special import expit  # This is the sigmoid function\n",
        "\n",
        "# Calculate the probability at the decision boundary\n",
        "probability = expit(b0 + b1 * decision_boundary)\n",
        "\n",
        "print(\"Probability of being Iris virginica at decision boundary:\", probability)\n",
        "print(\"Probability of not being Iris virginica at decision boundary:\", 1 - probability)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0N9kud7xobIM",
        "outputId": "5fde1bd1-3489-43b2-c705-07e91db66378"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Probability of being Iris virginica at decision boundary: 0.5008571303960639\n",
            "Probability of not being Iris virginica at decision boundary: 0.49914286960393606\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "log_reg.predict([[1.7], [1.5]])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uNY0He3KLnzy",
        "outputId": "6ea49450-2eed-46db-cdb3-4411e6d88f9a"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True, False])"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "When we use predict() function, the classifier would return whichever class (setosa, virginica, versicolor) is most likely.There is a decision boundary at around 1.6 cm and here, both probailities (virginca, not virginica) are equal to 50%. So, if the petal width is greater than 1.6 cm the classifier will predict that the flower is an Iris\n",
        "virginica(1), and otherwise it will predict that it is not (0) (even if it is not very confident)."
      ],
      "metadata": {
        "id": "Nc1vz9__IsaM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Code to check that the probability of belonging to each of the 2 classes( 'Virginca' and 'not virginca') is 50%.**"
      ],
      "metadata": {
        "id": "4WQ_Nzbym2Hf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from scipy.stats import logistic\n",
        "\n",
        "# Decision boundary found from the model\n",
        "decision_boundary = 1.6516516516516517\n",
        "\n",
        "# Logistic regression model would output probability 0.5 at the decision boundary\n",
        "# Using the logistic function (sigmoid function)\n",
        "# logistic.cdf returns the cumulative distribution function for logistic distribution\n",
        "\n",
        "probability = logistic.cdf(decision_boundary)\n",
        "probability_virginica = probability\n",
        "probability_not_virginica = 1 - probability\n",
        "\n",
        "print(\"Probability of being Iris virginica at decision boundary:\", probability_virginica)\n",
        "print(\"Probability of not being Iris virginica at decision boundary:\", probability_not_virginica)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WGwr1nTOm1Ai",
        "outputId": "4da43404-0e7a-4a9d-e014-2adc36577295"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Probability of being Iris virginica at decision boundary: 0.8391141509344003\n",
            "Probability of not being Iris virginica at decision boundary: 0.16088584906559966\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Support Vector Machines**"
      ],
      "metadata": {
        "id": "rhXvogdcNSMb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "SVM is a supervised machine learning model used for classification, regression and even novelty detection tasks. These are good with small to medium sized non-linear datasets especially for classification tasks. However, they don’t scale\n",
        "very well to very large datasets, which is why if you remember we had to use this classifier on only the first 20,000 instances of the MNIST dataset(70,000 images in total)."
      ],
      "metadata": {
        "id": "XBlNMoiANqBV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Linear SVM Classification**\n"
      ],
      "metadata": {
        "id": "jKwOYJhzPK8V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.svm import SVC\n",
        "from sklearn import datasets\n",
        "\n",
        "iris = datasets.load_iris(as_frame=True)\n",
        "X = iris.data[[\"petal length (cm)\", \"petal width (cm)\"]].values\n",
        "y = iris.target\n",
        "\n",
        "setosa_or_versicolor = (y == 0) | (y == 1)\n",
        "X = X[setosa_or_versicolor]\n",
        "y = y[setosa_or_versicolor]\n",
        "\n",
        "# SVM Classifier model\n",
        "svm_clf = SVC(kernel=\"linear\", C=1e100)\n",
        "svm_clf.fit(X, y)\n",
        "\n",
        "# Bad models\n",
        "x0 = np.linspace(0, 5.5, 200)\n",
        "pred_1 = 5 * x0 - 20\n",
        "pred_2 = x0 - 1.8\n",
        "pred_3 = 0.1 * x0 + 0.5\n",
        "\n",
        "def plot_svc_decision_boundary(svm_clf, xmin, xmax):\n",
        "    w = svm_clf.coef_[0]\n",
        "    b = svm_clf.intercept_[0]\n",
        "\n",
        "    # At the decision boundary, w0*x0 + w1*x1 + b = 0\n",
        "    # => x1 = -w0/w1 * x0 - b/w1\n",
        "    x0 = np.linspace(xmin, xmax, 200)\n",
        "    decision_boundary = -w[0] / w[1] * x0 - b / w[1]\n",
        "\n",
        "    margin = 1/w[1]\n",
        "    gutter_up = decision_boundary + margin\n",
        "    gutter_down = decision_boundary - margin\n",
        "    svs = svm_clf.support_vectors_\n",
        "\n",
        "    plt.plot(x0, decision_boundary, \"k-\", linewidth=2, zorder=-2)\n",
        "    plt.plot(x0, gutter_up, \"k--\", linewidth=2, zorder=-2)\n",
        "    plt.plot(x0, gutter_down, \"k--\", linewidth=2, zorder=-2)\n",
        "    plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#AAA',\n",
        "                zorder=-1)\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(10, 2.7), sharey=True)\n",
        "\n",
        "plt.sca(axes[0])\n",
        "plt.plot(x0, pred_1, \"g--\", linewidth=2)\n",
        "plt.plot(x0, pred_2, \"m-\", linewidth=2)\n",
        "plt.plot(x0, pred_3, \"r-\", linewidth=2)\n",
        "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris versicolor\")\n",
        "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris setosa\")\n",
        "plt.xlabel(\"Petal length\")\n",
        "plt.ylabel(\"Petal width\")\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.axis([0, 5.5, 0, 2])\n",
        "plt.gca().set_aspect(\"equal\")\n",
        "plt.grid()\n",
        "\n",
        "plt.sca(axes[1])\n",
        "plot_svc_decision_boundary(svm_clf, 0, 5.5)\n",
        "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n",
        "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\")\n",
        "plt.xlabel(\"Petal length\")\n",
        "plt.axis([0, 5.5, 0, 2])\n",
        "plt.gca().set_aspect(\"equal\")\n",
        "plt.grid()\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 213
        },
        "id": "Quzz0Pw4IH26",
        "outputId": "7130ac72-5604-4788-daea-6d8253384f5d"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x270 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Linear SVM classification tries to find the widest \"street\" between classes in a dataset. This is a benefit from just using a line to separate classes because it allows the algorithm to be more easily generalized for new instances.**\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "Fitting the largest \"street\" is called **'Large margin classification'**, and we use it classify future instances. Any addition of vectors off the street, would not affect the decision boundary of the classifier and it is fully supported by the vectors on the edge and hence the name, Support vector machine.\n",
        "\n",
        "Also, **feature scaling is very important for SVM.**\n",
        "\n"
      ],
      "metadata": {
        "id": "I4gkNtD2Qo3L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "Xs = np.array([[1, 50], [5, 20], [3, 80], [5, 60]]).astype(np.float64)\n",
        "ys = np.array([0, 0, 1, 1])\n",
        "svm_clf = SVC(kernel=\"linear\", C=100).fit(Xs, ys)\n",
        "\n",
        "scaler = StandardScaler()\n",
        "X_scaled = scaler.fit_transform(Xs)\n",
        "svm_clf_scaled = SVC(kernel=\"linear\", C=100).fit(X_scaled, ys)\n",
        "\n",
        "plt.figure(figsize=(9, 2.7))\n",
        "plt.subplot(121)\n",
        "plt.plot(Xs[:, 0][ys==1], Xs[:, 1][ys==1], \"bo\")\n",
        "plt.plot(Xs[:, 0][ys==0], Xs[:, 1][ys==0], \"ms\")\n",
        "plot_svc_decision_boundary(svm_clf, 0, 6)\n",
        "plt.xlabel(\"$x_0$\")\n",
        "plt.ylabel(\"$x_1$    \", rotation=0)\n",
        "plt.title(\"Unscaled\")\n",
        "plt.axis([0, 6, 0, 90])\n",
        "plt.grid()\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.plot(X_scaled[:, 0][ys==1], X_scaled[:, 1][ys==1], \"bo\")\n",
        "plt.plot(X_scaled[:, 0][ys==0], X_scaled[:, 1][ys==0], \"ms\")\n",
        "plot_svc_decision_boundary(svm_clf_scaled, -2, 2)\n",
        "plt.xlabel(\"$x'_0$\")\n",
        "plt.ylabel(\"$x'_1$  \", rotation=0)\n",
        "plt.title(\"Scaled\")\n",
        "plt.axis([-2, 2, -2, 2])\n",
        "plt.grid()\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "0k2IbMMmUhb8",
        "outputId": "c4773c8b-bd64-4fea-f141-8fb2f4125e96"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x270 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the left plot, the vertical scale is much larger than the\n",
        "horizontal scale, so the widest possible street is close to horizontal.\n",
        "After feature scaling (e.g., using Scikit-Learn’s StandardScaler),\n",
        "the decision boundary in the right plot looks much better."
      ],
      "metadata": {
        "id": "n1ZBOvnyb942"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**What is 'Hard Margin classification'?**\n",
        "\n",
        "It refers to a restriction that all instances must be off the street and on the side of correct class, which is impossible in case of datasets with outliers.\n",
        "\n",
        "So, firstly it only works if the dataset is linearly seperable and secondly, it is sensitive to outliers."
      ],
      "metadata": {
        "id": "tP2x03S5a9Lx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_outliers = np.array([[3.4, 1.3], [3.2, 0.8]])\n",
        "y_outliers = np.array([0, 0])\n",
        "Xo1 = np.concatenate([X, X_outliers[:1]], axis=0)\n",
        "yo1 = np.concatenate([y, y_outliers[:1]], axis=0)\n",
        "Xo2 = np.concatenate([X, X_outliers[1:]], axis=0)\n",
        "yo2 = np.concatenate([y, y_outliers[1:]], axis=0)\n",
        "\n",
        "svm_clf2 = SVC(kernel=\"linear\", C=10**9)\n",
        "svm_clf2.fit(Xo2, yo2)\n",
        "\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(10, 2.7), sharey=True)\n",
        "\n",
        "plt.sca(axes[0])\n",
        "plt.plot(Xo1[:, 0][yo1==1], Xo1[:, 1][yo1==1], \"bs\")\n",
        "plt.plot(Xo1[:, 0][yo1==0], Xo1[:, 1][yo1==0], \"yo\")\n",
        "plt.text(0.3, 1.0, \"Impossible!\", color=\"red\", fontsize=18)\n",
        "plt.xlabel(\"Petal length\")\n",
        "plt.ylabel(\"Petal width\")\n",
        "plt.annotate(\n",
        "    \"Outlier\",\n",
        "    xy=(X_outliers[0][0], X_outliers[0][1]),\n",
        "    xytext=(2.5, 1.7),\n",
        "    ha=\"center\",\n",
        "    arrowprops=dict(facecolor='black', shrink=0.1),\n",
        ")\n",
        "plt.axis([0, 5.5, 0, 2])\n",
        "plt.grid()\n",
        "\n",
        "plt.sca(axes[1])\n",
        "plt.plot(Xo2[:, 0][yo2==1], Xo2[:, 1][yo2==1], \"bs\")\n",
        "plt.plot(Xo2[:, 0][yo2==0], Xo2[:, 1][yo2==0], \"yo\")\n",
        "plot_svc_decision_boundary(svm_clf2, 0, 5.5)\n",
        "plt.xlabel(\"Petal length\")\n",
        "plt.annotate(\n",
        "    \"Outlier\",\n",
        "    xy=(X_outliers[1][0], X_outliers[1][1]),\n",
        "    xytext=(3.2, 0.08),\n",
        "    ha=\"center\",\n",
        "    arrowprops=dict(facecolor='black', shrink=0.1),\n",
        ")\n",
        "plt.axis([0, 5.5, 0, 2])\n",
        "plt.grid()\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "Haz8VjklIH0u",
        "outputId": "1c5274d4-9b62-452e-c275-378a8aa71364"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x270 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, an alternative to this is **Soft margin classification**, which tries to reduce the margin violations, while keeping the street as wide as possible.\n",
        "\n",
        "The **hyperparameter \"C\"** can be adjusted to increase or decrease the margin violations allowed, with more margin violations allowed creating a wider \"street\" and a smaller \"C\" and fewer violations allowed creating a smaller street and larger \"C\". If the SVM model is overfitting then you can regularize it by reducing \"C\"."
      ],
      "metadata": {
        "id": "7mAo8bIeeAAI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import LinearSVC\n",
        "\n",
        "iris = load_iris(as_frame=True)\n",
        "X = iris.data[[\"petal length (cm)\", \"petal width (cm)\"]].values\n",
        "y = (iris.target == 2)  # Iris virginica\n",
        "\n",
        "svm_clf = make_pipeline(StandardScaler(),\n",
        "                        LinearSVC(C=1, dual=True, random_state=42))\n",
        "svm_clf.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "wz8Uw6LTIHyh",
        "outputId": "a5506ae4-809c-4ee4-a38e-489358fbd9ce"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                ('linearsvc', LinearSVC(C=1, random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvc&#x27;, LinearSVC(C=1, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvc&#x27;, LinearSVC(C=1, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(C=1, random_state=42)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_new = [[5.5, 1.7], [5.0, 1.5]]\n",
        "svm_clf.predict(X_new)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oYZu9KmWIHwC",
        "outputId": "077f2290-ea6a-4288-809f-32bb4654074b"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True, False])"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "svm_clf.decision_function(X_new)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WccCW1rZIHt2",
        "outputId": "bd50f028-fe18-4a29-8864-b78408401230"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.66163411, -0.22036063])"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "scaler = StandardScaler()\n",
        "svm_clf1 = LinearSVC(C=1, max_iter=10_000, dual=True, random_state=42)\n",
        "svm_clf2 = LinearSVC(C=100, max_iter=10_000, dual=True, random_state=42)\n",
        "\n",
        "scaled_svm_clf1 = make_pipeline(scaler, svm_clf1)\n",
        "scaled_svm_clf2 = make_pipeline(scaler, svm_clf2)\n",
        "\n",
        "scaled_svm_clf1.fit(X, y)\n",
        "scaled_svm_clf2.fit(X, y)\n",
        "\n",
        "# Convert to unscaled parameters\n",
        "b1 = svm_clf1.decision_function([-scaler.mean_ / scaler.scale_])\n",
        "b2 = svm_clf2.decision_function([-scaler.mean_ / scaler.scale_])\n",
        "w1 = svm_clf1.coef_[0] / scaler.scale_\n",
        "w2 = svm_clf2.coef_[0] / scaler.scale_\n",
        "svm_clf1.intercept_ = np.array([b1])\n",
        "svm_clf2.intercept_ = np.array([b2])\n",
        "svm_clf1.coef_ = np.array([w1])\n",
        "svm_clf2.coef_ = np.array([w2])\n",
        "\n",
        "# Find support vectors (LinearSVC does not do this automatically)\n",
        "t = y * 2 - 1\n",
        "support_vectors_idx1 = (t * (X.dot(w1) + b1) < 1).ravel()\n",
        "support_vectors_idx2 = (t * (X.dot(w2) + b2) < 1).ravel()\n",
        "svm_clf1.support_vectors_ = X[support_vectors_idx1]\n",
        "svm_clf2.support_vectors_ = X[support_vectors_idx2]\n",
        "\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(10, 2.7), sharey=True)\n",
        "\n",
        "plt.sca(axes[0])\n",
        "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\", label=\"Iris virginica\")\n",
        "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\", label=\"Iris versicolor\")\n",
        "plot_svc_decision_boundary(svm_clf1, 4, 5.9)\n",
        "plt.xlabel(\"Petal length\")\n",
        "plt.ylabel(\"Petal width\")\n",
        "plt.legend(loc=\"upper left\")\n",
        "plt.title(f\"$C = {svm_clf1.C}$\")\n",
        "plt.axis([4, 5.9, 0.8, 2.8])\n",
        "plt.grid()\n",
        "\n",
        "plt.sca(axes[1])\n",
        "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
        "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
        "plot_svc_decision_boundary(svm_clf2, 4, 5.99)\n",
        "plt.xlabel(\"Petal length\")\n",
        "plt.title(f\"$C = {svm_clf2.C}$\")\n",
        "plt.axis([4, 5.9, 0.8, 2.8])\n",
        "plt.grid()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "lVqUzVNqIHrn",
        "outputId": "3c2e7743-b513-4a06-b643-9a5a874d1cb7"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x270 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Note : Unlike LogisticRegression, LinearSVC doesn’t have a predict_proba() method to estimate the class probabilities. That said, if you use the SVC class (discussed shortly)\n",
        "instead of LinearSVC, and if you set its probability hyperparameter to True, then\n",
        "the model will fit an extra model at the end of training to map the SVM decision\n",
        "function scores to estimated probabilities.**"
      ],
      "metadata": {
        "id": "wPEDJ-EBlGrZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Nonlinear SVM Classification**"
      ],
      "metadata": {
        "id": "0mQM7qjvjA1i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# extra code – this cell generates and saves Figure 5–5\n",
        "\n",
        "X1D = np.linspace(-4, 4, 9).reshape(-1, 1)\n",
        "X2D = np.c_[X1D, X1D**2]\n",
        "y = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
        "\n",
        "plt.figure(figsize=(10, 3))\n",
        "\n",
        "plt.subplot(121)\n",
        "plt.grid(True)\n",
        "plt.axhline(y=0, color='k')\n",
        "plt.plot(X1D[:, 0][y==0], np.zeros(4), \"bs\")\n",
        "plt.plot(X1D[:, 0][y==1], np.zeros(5), \"g^\")\n",
        "plt.gca().get_yaxis().set_ticks([])\n",
        "plt.xlabel(\"$x_1$\")\n",
        "plt.axis([-4.5, 4.5, -0.2, 0.2])\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.grid(True)\n",
        "plt.axhline(y=0, color='k')\n",
        "plt.axvline(x=0, color='k')\n",
        "plt.plot(X2D[:, 0][y==0], X2D[:, 1][y==0], \"bs\")\n",
        "plt.plot(X2D[:, 0][y==1], X2D[:, 1][y==1], \"g^\")\n",
        "plt.xlabel(\"$x_1$\")\n",
        "plt.ylabel(\"$x_2$  \", rotation=0)\n",
        "plt.gca().get_yaxis().set_ticks([0, 4, 8, 12, 16])\n",
        "plt.plot([-4.5, 4.5], [6.5, 6.5], \"r--\", linewidth=3)\n",
        "plt.axis([-4.5, 4.5, -1, 17])\n",
        "\n",
        "plt.subplots_adjust(right=1)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 311
        },
        "id": "TvBoqCJZIHpD",
        "outputId": "995edb91-2173-45ae-f3ce-e5c56b52fc69"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x300 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To implement this, we use moons dataset, which is a toy dataset for binary classification in which the data points are shaped as two interleaving crescent moons."
      ],
      "metadata": {
        "id": "pFDPHNUKpheu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.datasets import make_moons\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.svm import LinearSVC\n",
        "\n",
        "X, y = make_moons(n_samples=100, noise=0.15, random_state=42)\n",
        "\n",
        "polynomial_svm_clf = make_pipeline(\n",
        "    PolynomialFeatures(degree=3),\n",
        "    StandardScaler(),\n",
        "    LinearSVC(C=10, max_iter=10_000, dual=True, random_state=42)\n",
        ")\n",
        "polynomial_svm_clf.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "id": "jV6iS5LOqJ2-",
        "outputId": "489edbba-3ddf-4f73-fc3c-6729853f62d0"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('polynomialfeatures', PolynomialFeatures(degree=3)),\n",
              "                ('standardscaler', StandardScaler()),\n",
              "                ('linearsvc',\n",
              "                 LinearSVC(C=10, max_iter=10000, random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-15 {color: black;background-color: white;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-15 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-15 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;polynomialfeatures&#x27;, PolynomialFeatures(degree=3)),\n",
              "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvc&#x27;,\n",
              "                 LinearSVC(C=10, max_iter=10000, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;polynomialfeatures&#x27;, PolynomialFeatures(degree=3)),\n",
              "                (&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvc&#x27;,\n",
              "                 LinearSVC(C=10, max_iter=10000, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">PolynomialFeatures</label><div class=\"sk-toggleable__content\"><pre>PolynomialFeatures(degree=3)</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC(C=10, max_iter=10000, random_state=42)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# extra code – this cell generates and saves Figure 5–6\n",
        "\n",
        "def plot_dataset(X, y, axes):\n",
        "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
        "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
        "    plt.axis(axes)\n",
        "    plt.grid(True)\n",
        "    plt.xlabel(\"$x_1$\")\n",
        "    plt.ylabel(\"$x_2$\", rotation=0)\n",
        "\n",
        "def plot_predictions(clf, axes):\n",
        "    x0s = np.linspace(axes[0], axes[1], 100)\n",
        "    x1s = np.linspace(axes[2], axes[3], 100)\n",
        "    x0, x1 = np.meshgrid(x0s, x1s)\n",
        "    X = np.c_[x0.ravel(), x1.ravel()]\n",
        "    y_pred = clf.predict(X).reshape(x0.shape)\n",
        "    y_decision = clf.decision_function(X).reshape(x0.shape)\n",
        "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
        "    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n",
        "\n",
        "plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
        "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "fIx3F1JPIHmp",
        "outputId": "55beeba7-e446-4228-b4fb-de529f306e5e"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**But, at a low polynomial\n",
        "degree this method cannot deal with very complex datasets (and you might not have enough differentiation to separate), and with a high polynomial degree it creates a huge number of features, making the model too slow.**"
      ],
      "metadata": {
        "id": "TbmWsXfeq8X8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**KERNEL TRICK**:\n",
        "\n",
        "The kernel trick makes it possible to get the same result as if you had added many polynomial\n",
        "features, even with a very high degree, without actually having to add them.\n",
        "\n",
        "This helps reduce the computational complexity of your model and it is implemented by SVC class.\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Rj-7KGE5rE0e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "poly_kernel_svm_clf = make_pipeline(StandardScaler(),\n",
        "                                    SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
        "poly_kernel_svm_clf.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "eVIvg4JBIHkr",
        "outputId": "fe497468-5bdc-4229-b0a0-cb2dfba081f8"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                ('svc', SVC(C=5, coef0=1, kernel='poly'))])"
            ],
            "text/html": [
              "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;svc&#x27;, SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;svc&#x27;, SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=5, coef0=1, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "poly100_kernel_svm_clf = make_pipeline(\n",
        "    StandardScaler(),\n",
        "    SVC(kernel=\"poly\", degree=10, coef0=100, C=5)\n",
        ")\n",
        "poly100_kernel_svm_clf.fit(X, y)\n",
        "\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(10.5, 4), sharey=True)\n",
        "\n",
        "plt.sca(axes[0])\n",
        "plot_predictions(poly_kernel_svm_clf, [-1.5, 2.45, -1, 1.5])\n",
        "plot_dataset(X, y, [-1.5, 2.4, -1, 1.5])\n",
        "plt.title(\"degree=3, coef0=1, C=5\")\n",
        "\n",
        "plt.sca(axes[1])\n",
        "plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.45, -1, 1.5])\n",
        "plot_dataset(X, y, [-1.5, 2.4, -1, 1.5])\n",
        "plt.title(\"degree=10, coef0=100, C=5\")\n",
        "plt.ylabel(\"\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "Wba_3VX0IHid",
        "outputId": "21b7ba4a-9db6-4a69-8957-2b86fddf9686"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1050x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**coef0=1:**\n",
        "\n",
        "This hyperparameter controls the influence of high-degree polynomial terms.\n",
        "This means that the higher-degree terms (terms resulting from the polynomial expansion) have more weight in the final computation of the kernel function.\n",
        "\n",
        "The decision boundary will be more complex, as the model will place more emphasis on higher-order interactions between the features.\n",
        "This can help in capturing more complex patterns in the data (unlike the low coef) but, also increases the risk of overfitting if the degree is high and coef0 is too large.\n",
        "\n"
      ],
      "metadata": {
        "id": "PmMTkYt8uUN_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**C :Regularization parameter**\n",
        "A higher value of C means the model tries to fit the training data more precisely (less regularization).\n",
        "A lower value of C means more regularization and can help with avoiding overfitting."
      ],
      "metadata": {
        "id": "AN-tTDBO1zwr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Handling Overfitting and Underfitting:**\n",
        "\n",
        "Overfitting:\n",
        "Reduce the polynomial degree.\n",
        "Increase the regularization parameter C.\n",
        "\n",
        "\n",
        "Underfitting:\n",
        "Increase the polynomial degree.\n",
        "Decrease the regularization parameter C."
      ],
      "metadata": {
        "id": "m1jDr--h2LSN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Create and train the SVM with polynomial kernel\n",
        "poly50_kernel_svm_clf = make_pipeline(\n",
        "    StandardScaler(),\n",
        "    SVC(kernel=\"poly\", degree=5, coef0=50, C=5)\n",
        ")\n",
        "poly50_kernel_svm_clf.fit(X, y)\n",
        "\n",
        "# Plot the results\n",
        "plt.figure(figsize=(6, 4))\n",
        "plot_predictions(poly50_kernel_svm_clf, [-1.5, 2.45, -1, 1.5])\n",
        "plot_dataset(X, y, [-1.5, 2.4, -1, 1.5])\n",
        "plt.title(\"degree=5, coef0=50, C=5\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "ZhOkAACYwSyE",
        "outputId": "9de7b4a8-5277-46b9-a808-f294b4d7efb0"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**By using a polynomial kernel with a degree of 5 and coef0 of 50, the model has a high degree of flexibility, allowing it to fit the data more closely.**\n"
      ],
      "metadata": {
        "id": "ESstbAYwzjet"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Another approach is to add features computed by the **Similarity function**:"
      ],
      "metadata": {
        "id": "9o10no_l3A5y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def gaussian_rbf(x, landmark, gamma):\n",
        "    return np.exp(-gamma * np.linalg.norm(x - landmark, axis=1)**2)\n",
        "\n",
        "gamma = 0.3\n",
        "\n",
        "x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\n",
        "x2s = gaussian_rbf(x1s, -2, gamma)\n",
        "x3s = gaussian_rbf(x1s, 1, gamma)\n",
        "\n",
        "XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\n",
        "yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
        "\n",
        "plt.figure(figsize=(10.5, 4))\n",
        "\n",
        "plt.subplot(121)\n",
        "plt.grid(True)\n",
        "plt.axhline(y=0, color='k')\n",
        "plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c=\"red\")\n",
        "plt.plot(X1D[:, 0][yk==0], np.zeros(4), \"bs\")\n",
        "plt.plot(X1D[:, 0][yk==1], np.zeros(5), \"g^\")\n",
        "plt.plot(x1s, x2s, \"g--\")\n",
        "plt.plot(x1s, x3s, \"b:\")\n",
        "plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\n",
        "plt.xlabel(\"$x_1$\")\n",
        "plt.ylabel(\"Similarity\")\n",
        "plt.annotate(\n",
        "    r'$\\mathbf{x}$',\n",
        "    xy=(X1D[3, 0], 0),\n",
        "    xytext=(-0.5, 0.20),\n",
        "    ha=\"center\",\n",
        "    arrowprops=dict(facecolor='black', shrink=0.1),\n",
        "    fontsize=16,\n",
        ")\n",
        "plt.text(-2, 0.9, \"$x_2$\", ha=\"center\", fontsize=15)\n",
        "plt.text(1, 0.9, \"$x_3$\", ha=\"center\", fontsize=15)\n",
        "plt.axis([-4.5, 4.5, -0.1, 1.1])\n",
        "\n",
        "plt.subplot(122)\n",
        "plt.grid(True)\n",
        "plt.axhline(y=0, color='k')\n",
        "plt.axvline(x=0, color='k')\n",
        "plt.plot(XK[:, 0][yk==0], XK[:, 1][yk==0], \"bs\")\n",
        "plt.plot(XK[:, 0][yk==1], XK[:, 1][yk==1], \"g^\")\n",
        "plt.xlabel(\"$x_2$\")\n",
        "plt.ylabel(\"$x_3$  \", rotation=0)\n",
        "plt.annotate(\n",
        "    r'$\\phi\\left(\\mathbf{x}\\right)$',\n",
        "    xy=(XK[3, 0], XK[3, 1]),\n",
        "    xytext=(0.65, 0.50),\n",
        "    ha=\"center\",\n",
        "    arrowprops=dict(facecolor='black', shrink=0.1),\n",
        "    fontsize=16,\n",
        ")\n",
        "plt.plot([-0.1, 1.1], [0.57, -0.1], \"r--\", linewidth=3)\n",
        "plt.axis([-0.1, 1.1, -0.1, 1.1])\n",
        "\n",
        "plt.subplots_adjust(right=1)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "q4pslkSWIHfu",
        "outputId": "ea196ab1-eabd-4b2e-a48a-d1123164e214"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1050x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Similarity function,  measures how much each instance resembles a particular\n",
        "landmark. In the first  plot (1D), we add 2 landmarks to it at x1=–2 and x1 = 1."
      ],
      "metadata": {
        "id": "v5vC2BTv6SVS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " look at the instance\n",
        "x1= –1: it is located at a distance of 1 from the first landmark and 2 from the second landmark. Therefore, its new features are x2 = exp(–0.3 × 12) ≈ 0.74 and x3= exp(–0.3× 22) ≈ 0.30.\n",
        "The plot on the right shows the transformed dataset(dropping the original features) and it is now linearly separable.\n",
        "\n",
        "But again, if the dataset is very large we end up with creation of large number of features making the model computationally expensive."
      ],
      "metadata": {
        "id": "Zsl1iNGI6wo2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**To deal with this issue, we use the Gaussian RBF Kernel: where we can obtain the same result as if we had added many similarity features but without doing so**"
      ],
      "metadata": {
        "id": "ZoqzkCv38JmL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Gaussian RBF Kernel**"
      ],
      "metadata": {
        "id": "c0KRSp_S8ofe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rbf_kernel_svm_clf = make_pipeline(StandardScaler(),\n",
        "                                   SVC(kernel=\"rbf\", gamma=5, C=0.001))\n",
        "rbf_kernel_svm_clf.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "7r4IIYYXIHar",
        "outputId": "80cc3e6e-35fb-49b6-b59d-7d06d33e2373"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                ('svc', SVC(C=0.001, gamma=5))])"
            ],
            "text/html": [
              "<style>#sk-container-id-17 {color: black;background-color: white;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-17 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-17 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;svc&#x27;, SVC(C=0.001, gamma=5))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-44\" type=\"checkbox\" ><label for=\"sk-estimator-id-44\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;svc&#x27;, SVC(C=0.001, gamma=5))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-45\" type=\"checkbox\" ><label for=\"sk-estimator-id-45\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-46\" type=\"checkbox\" ><label for=\"sk-estimator-id-46\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=0.001, gamma=5)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# extra code – this cell generates and saves Figure 5–9\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "gamma1, gamma2 = 0.1, 5\n",
        "C1, C2 = 0.001, 1000\n",
        "hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\n",
        "\n",
        "svm_clfs = []\n",
        "for gamma, C in hyperparams:\n",
        "    rbf_kernel_svm_clf = make_pipeline(\n",
        "        StandardScaler(),\n",
        "        SVC(kernel=\"rbf\", gamma=gamma, C=C)\n",
        "    )\n",
        "    rbf_kernel_svm_clf.fit(X, y)\n",
        "    svm_clfs.append(rbf_kernel_svm_clf)\n",
        "\n",
        "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10.5, 7), sharex=True, sharey=True)\n",
        "\n",
        "for i, svm_clf in enumerate(svm_clfs):\n",
        "    plt.sca(axes[i // 2, i % 2])\n",
        "    plot_predictions(svm_clf, [-1.5, 2.45, -1, 1.5])\n",
        "    plot_dataset(X, y, [-1.5, 2.45, -1, 1.5])\n",
        "    gamma, C = hyperparams[i]\n",
        "    plt.title(f\"gamma={gamma}, C={C}\")\n",
        "    if i in (0, 1):\n",
        "        plt.xlabel(\"\")\n",
        "    if i in (1, 3):\n",
        "        plt.ylabel(\"\")\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 642
        },
        "id": "_foWPGMTIHYF",
        "outputId": "ef275854-98c3-45c1-a9a0-e2486d3b7950"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1050x700 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SVM Regression**"
      ],
      "metadata": {
        "id": "cdTrHPVf-2jU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "SVM algorithms can also be used for linear and non-linear regression."
      ],
      "metadata": {
        "id": "VZmEAvNSA7ab"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "SVM Regressors tend to do the opposite of classifiers. These Regressors will attempt to fit as many instances **on the street** as it can, (instead of fitting the largest possible street between 2 classes) while limiting margin violations."
      ],
      "metadata": {
        "id": "lGhAd2NX-2eE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**The width\n",
        "of the street is controlled by a hyperparameter, ϵ.**"
      ],
      "metadata": {
        "id": "Eyz592Ad-2ar"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.svm import LinearSVR\n",
        "\n",
        "# extra code – these 3 lines generate a simple linear dataset\n",
        "np.random.seed(42)\n",
        "X = 2 * np.random.rand(50, 1)\n",
        "y = 4 + 3 * X[:, 0] + np.random.randn(50)\n",
        "\n",
        "svm_reg = make_pipeline(StandardScaler(),\n",
        "                        LinearSVR(epsilon=0.5, dual=True, random_state=42))\n",
        "svm_reg.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "Upk69nagIHUa",
        "outputId": "cff4bf1d-6791-4522-bb15-3da1dd723287"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                ('linearsvr', LinearSVR(epsilon=0.5, random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-18 {color: black;background-color: white;}#sk-container-id-18 pre{padding: 0;}#sk-container-id-18 div.sk-toggleable {background-color: white;}#sk-container-id-18 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-18 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-18 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-18 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-18 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-18 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-18 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-18 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-18 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-18 div.sk-item {position: relative;z-index: 1;}#sk-container-id-18 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-18 div.sk-item::before, #sk-container-id-18 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-18 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-18 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-18 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-18 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-18 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-18 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-18 div.sk-label-container {text-align: center;}#sk-container-id-18 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-18 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvr&#x27;, LinearSVR(epsilon=0.5, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-47\" type=\"checkbox\" ><label for=\"sk-estimator-id-47\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;linearsvr&#x27;, LinearSVR(epsilon=0.5, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-48\" type=\"checkbox\" ><label for=\"sk-estimator-id-48\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-49\" type=\"checkbox\" ><label for=\"sk-estimator-id-49\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVR</label><div class=\"sk-toggleable__content\"><pre>LinearSVR(epsilon=0.5, random_state=42)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def find_support_vectors(svm_reg, X, y):\n",
        "    y_pred = svm_reg.predict(X)\n",
        "    epsilon = svm_reg[-1].epsilon\n",
        "    off_margin = np.abs(y - y_pred) >= epsilon\n",
        "    return np.argwhere(off_margin)\n",
        "\n",
        "def plot_svm_regression(svm_reg, X, y, axes):\n",
        "    x1s = np.linspace(axes[0], axes[1], 100).reshape(100, 1)\n",
        "    y_pred = svm_reg.predict(x1s)\n",
        "    epsilon = svm_reg[-1].epsilon\n",
        "    plt.plot(x1s, y_pred, \"k-\", linewidth=2, label=r\"$\\hat{y}$\", zorder=-2)\n",
        "    plt.plot(x1s, y_pred + epsilon, \"k--\", zorder=-2)\n",
        "    plt.plot(x1s, y_pred - epsilon, \"k--\", zorder=-2)\n",
        "    plt.scatter(X[svm_reg._support], y[svm_reg._support], s=180,\n",
        "                facecolors='#AAA', zorder=-1)\n",
        "    plt.plot(X, y, \"bo\")\n",
        "    plt.xlabel(\"$x_1$\")\n",
        "    plt.legend(loc=\"upper left\")\n",
        "    plt.axis(axes)\n",
        "\n",
        "svm_reg2 = make_pipeline(StandardScaler(),\n",
        "                         LinearSVR(epsilon=1.2, dual=True, random_state=42))\n",
        "svm_reg2.fit(X, y)\n",
        "\n",
        "svm_reg._support = find_support_vectors(svm_reg, X, y)\n",
        "svm_reg2._support = find_support_vectors(svm_reg2, X, y)\n",
        "\n",
        "eps_x1 = 1\n",
        "eps_y_pred = svm_reg2.predict([[eps_x1]])\n",
        "\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(9, 4), sharey=True)\n",
        "plt.sca(axes[0])\n",
        "plot_svm_regression(svm_reg, X, y, [0, 2, 3, 11])\n",
        "plt.title(f\"epsilon={svm_reg[-1].epsilon}\")\n",
        "plt.ylabel(\"$y$\", rotation=0)\n",
        "plt.grid()\n",
        "plt.sca(axes[1])\n",
        "plot_svm_regression(svm_reg2, X, y, [0, 2, 3, 11])\n",
        "plt.title(f\"epsilon={svm_reg2[-1].epsilon}\")\n",
        "plt.annotate(\n",
        "        '', xy=(eps_x1, eps_y_pred), xycoords='data',\n",
        "        xytext=(eps_x1, eps_y_pred - svm_reg2[-1].epsilon),\n",
        "        textcoords='data', arrowprops={'arrowstyle': '<->', 'linewidth': 1.5}\n",
        "    )\n",
        "plt.text(0.90, 5.4, r\"$\\epsilon$\", fontsize=16)\n",
        "plt.grid()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "XFB8SHmZIGMy",
        "outputId": "dddd799b-d2d8-4d76-e7c8-6ab6b13b475d"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**On Linear Data**"
      ],
      "metadata": {
        "id": "rxFaZt30DkGF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The above 2 plots show two linear SVM\n",
        "regression models trained on some ** linear data,**  one with a small margin (ϵ = 0.5) and\n",
        "the other with a larger margin (ϵ = 1.2)"
      ],
      "metadata": {
        "id": "eer7gDtmCfC4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Inference:**\n",
        "\n",
        "By reducing epsilon, the number of support vectors increase, which regularizes the model.\n",
        "\n",
        "Also, adding more instances within the margin will not effect the model's predictions, thus the model is known as epsilon-insensitive."
      ],
      "metadata": {
        "id": "hpRXYuvBCmCP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**On Non-linear Data**:"
      ],
      "metadata": {
        "id": "Q9gPBiNgDpF5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.svm import SVR\n",
        "\n",
        "# extra code – these 3 lines generate a simple quadratic dataset\n",
        "np.random.seed(42)\n",
        "X = 2 * np.random.rand(50, 1) - 1\n",
        "y = 0.2 + 0.1 * X[:, 0] + 0.5 * X[:, 0] ** 2 + np.random.randn(50) / 10\n",
        "\n",
        "svm_poly_reg = make_pipeline(StandardScaler(),\n",
        "                             SVR(kernel=\"poly\", degree=2, C=0.01, epsilon=0.1))\n",
        "svm_poly_reg.fit(X, y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "24QeNIgdCMge",
        "outputId": "33e737cd-70a1-47af-c0e4-3d3869df86b2"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('standardscaler', StandardScaler()),\n",
              "                ('svr', SVR(C=0.01, degree=2, kernel='poly'))])"
            ],
            "text/html": [
              "<style>#sk-container-id-19 {color: black;background-color: white;}#sk-container-id-19 pre{padding: 0;}#sk-container-id-19 div.sk-toggleable {background-color: white;}#sk-container-id-19 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-19 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-19 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-19 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-19 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-19 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-19 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-19 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-19 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-19 div.sk-item {position: relative;z-index: 1;}#sk-container-id-19 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-19 div.sk-item::before, #sk-container-id-19 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-19 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-19 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-19 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-19 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-19 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-19 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-19 div.sk-label-container {text-align: center;}#sk-container-id-19 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-19 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;svr&#x27;, SVR(C=0.01, degree=2, kernel=&#x27;poly&#x27;))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-50\" type=\"checkbox\" ><label for=\"sk-estimator-id-50\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;standardscaler&#x27;, StandardScaler()),\n",
              "                (&#x27;svr&#x27;, SVR(C=0.01, degree=2, kernel=&#x27;poly&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-51\" type=\"checkbox\" ><label for=\"sk-estimator-id-51\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-52\" type=\"checkbox\" ><label for=\"sk-estimator-id-52\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR(C=0.01, degree=2, kernel=&#x27;poly&#x27;)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "svm_poly_reg2 = make_pipeline(StandardScaler(),\n",
        "                             SVR(kernel=\"poly\", degree=2, C=100))\n",
        "svm_poly_reg2.fit(X, y)\n",
        "\n",
        "svm_poly_reg._support = find_support_vectors(svm_poly_reg, X, y)\n",
        "svm_poly_reg2._support = find_support_vectors(svm_poly_reg2, X, y)\n",
        "\n",
        "fig, axes = plt.subplots(ncols=2, figsize=(9, 4), sharey=True)\n",
        "plt.sca(axes[0])\n",
        "plot_svm_regression(svm_poly_reg, X, y, [-1, 1, 0, 1])\n",
        "plt.title(f\"degree={svm_poly_reg[-1].degree}, \"\n",
        "          f\"C={svm_poly_reg[-1].C}, \"\n",
        "          f\"epsilon={svm_poly_reg[-1].epsilon}\")\n",
        "plt.ylabel(\"$y$\", rotation=0)\n",
        "plt.grid()\n",
        "\n",
        "plt.sca(axes[1])\n",
        "plot_svm_regression(svm_poly_reg2, X, y, [-1, 1, 0, 1])\n",
        "plt.title(f\"degree={svm_poly_reg2[-1].degree}, \"\n",
        "          f\"C={svm_poly_reg2[-1].C}, \"\n",
        "          f\"epsilon={svm_poly_reg2[-1].epsilon}\")\n",
        "plt.grid()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "CsVhO790EJKY",
        "outputId": "25e1d05c-413d-4e48-df10-27a141a14dc5"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Like in the case of classification tasks, we can use the  SVR class (which supports the kernel trick) to\n",
        "produce the model represented on the left which depicts some regularization (small C value) than the plot on the right(large c value)."
      ],
      "metadata": {
        "id": "I3BHlm8yEZQx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Conclusion:**\n",
        "\n",
        "**The SVR class is the regression equivalent of the SVC class, and the LinearSVR class is the regression equivalent of the LinearSVC class.**\n",
        "\n",
        "\n",
        "**The LinearSVR class scales linearly\n",
        "with the size of the training set (just like the LinearSVC class), while the SVR class gets much too slow when the training set grows very large (just like the SVC class).**\n"
      ],
      "metadata": {
        "id": "2v8af3ahI_1i"
      }
    }
  ]
}