Notebook/randomforest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "from sklearn.datasets import make_classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = make_classification(n_features=5, n_redundant=0,n_informative=5, n_clusters_per_class=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(X,columns=['col1','col2','col3','col4','col5'])\n",
    "df['target'] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for row sampling\n",
    "import random\n",
    "def sample_rows(df,percent):\n",
    "    return df.sample(int(percent*df.shape[0]), replace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_rows(df,0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for feature sampling\n",
    "def sample_features(df,percent):\n",
    "    cols = random.sample(df.columns.tolist()[:-1],int(percent*(df.shape[1]-1)))\n",
    "    new_df = df[cols]\n",
    "    new_df['target'] = df['target']\n",
    "    return new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_features(df,0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# combined sampling\n",
    "def combined_sampling(df, row_percent, col_percent):\n",
    "    new_df = sample_rows(df,row_percent)\n",
    "    return sample_features(new_df, col_percent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\akkan\\AppData\\Local\\Temp\\ipykernel_13528\\3999859604.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df['target'] = df['target']\n"
     ]
    }
   ],
   "source": [
    "df1 = combined_sampling(df,0.5,0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\akkan\\AppData\\Local\\Temp\\ipykernel_13528\\3999859604.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df['target'] = df['target']\n"
     ]
    }
   ],
   "source": [
    "df2 = combined_sampling(df,0.5,0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\akkan\\AppData\\Local\\Temp\\ipykernel_13528\\3999859604.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  new_df['target'] = df['target']\n"
     ]
    }
   ],
   "source": [
    "df3 = combined_sampling(df,0.5,0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['col1', 'col5', 'target'], dtype='object')\n",
      "Index(['col5', 'col4', 'target'], dtype='object')\n",
      "Index(['col4', 'col1', 'target'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df1.columns)\n",
    "print(df2.columns)\n",
    "print(df3.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf1 = DecisionTreeClassifier()\n",
    "clf2 = DecisionTreeClassifier()\n",
    "clf3 = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</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 fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf1.fit(df1.iloc[:,0:2],df1.iloc[:,-1])\n",
    "clf2.fit(df2.iloc[:,0:2],df2.iloc[:,-1])\n",
    "clf3.fit(df3.iloc[:,0:2],df3.iloc[:,-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import plot_tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.39285714285714285, 0.9, 'x[0] <= 0.638\\ngini = 0.461\\nsamples = 50\\nvalue = [32, 18]'),\n",
       " Text(0.14285714285714285, 0.7, 'x[0] <= 0.094\\ngini = 0.124\\nsamples = 30\\nvalue = [28, 2]'),\n",
       " Text(0.26785714285714285, 0.8, 'True  '),\n",
       " Text(0.07142857142857142, 0.5, 'gini = 0.0\\nsamples = 23\\nvalue = [23, 0]'),\n",
       " Text(0.21428571428571427, 0.5, 'x[1] <= -0.389\\ngini = 0.408\\nsamples = 7\\nvalue = [5, 2]'),\n",
       " Text(0.14285714285714285, 0.3, 'gini = 0.0\\nsamples = 5\\nvalue = [5, 0]'),\n",
       " Text(0.2857142857142857, 0.3, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.6428571428571429, 0.7, 'x[1] <= 0.562\\ngini = 0.32\\nsamples = 20\\nvalue = [4, 16]'),\n",
       " Text(0.5178571428571429, 0.8, '  False'),\n",
       " Text(0.5, 0.5, 'x[1] <= 0.169\\ngini = 0.48\\nsamples = 5\\nvalue = [3, 2]'),\n",
       " Text(0.42857142857142855, 0.3, 'x[0] <= 1.502\\ngini = 0.444\\nsamples = 3\\nvalue = [1, 2]'),\n",
       " Text(0.35714285714285715, 0.1, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.5, 0.1, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.5714285714285714, 0.3, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
       " Text(0.7857142857142857, 0.5, 'x[0] <= 1.511\\ngini = 0.124\\nsamples = 15\\nvalue = [1, 14]'),\n",
       " Text(0.7142857142857143, 0.3, 'gini = 0.0\\nsamples = 9\\nvalue = [0, 9]'),\n",
       " Text(0.8571428571428571, 0.3, 'x[0] <= 1.567\\ngini = 0.278\\nsamples = 6\\nvalue = [1, 5]'),\n",
       " Text(0.7857142857142857, 0.1, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.9285714285714286, 0.1, 'gini = 0.0\\nsamples = 5\\nvalue = [0, 5]')]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tree(clf1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5714285714285714, 0.9375, 'x[0] <= -0.486\\ngini = 0.48\\nsamples = 50\\nvalue = [20, 30]'),\n",
       " Text(0.42857142857142855, 0.8125, 'gini = 0.0\\nsamples = 11\\nvalue = [11, 0]'),\n",
       " Text(0.5, 0.875, 'True  '),\n",
       " Text(0.7142857142857143, 0.8125, 'x[0] <= 1.858\\ngini = 0.355\\nsamples = 39\\nvalue = [9, 30]'),\n",
       " Text(0.6428571428571428, 0.875, '  False'),\n",
       " Text(0.5714285714285714, 0.6875, 'x[1] <= 1.295\\ngini = 0.307\\nsamples = 37\\nvalue = [7, 30]'),\n",
       " Text(0.42857142857142855, 0.5625, 'x[0] <= 0.704\\ngini = 0.403\\nsamples = 25\\nvalue = [7.0, 18.0]'),\n",
       " Text(0.2857142857142857, 0.4375, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
       " Text(0.5714285714285714, 0.4375, 'x[1] <= 1.18\\ngini = 0.298\\nsamples = 22\\nvalue = [4, 18]'),\n",
       " Text(0.42857142857142855, 0.3125, 'x[1] <= -0.661\\ngini = 0.1\\nsamples = 19\\nvalue = [1, 18]'),\n",
       " Text(0.2857142857142857, 0.1875, 'x[1] <= -0.787\\ngini = 0.444\\nsamples = 3\\nvalue = [1, 2]'),\n",
       " Text(0.14285714285714285, 0.0625, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.42857142857142855, 0.0625, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.5714285714285714, 0.1875, 'gini = 0.0\\nsamples = 16\\nvalue = [0, 16]'),\n",
       " Text(0.7142857142857143, 0.3125, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
       " Text(0.7142857142857143, 0.5625, 'gini = 0.0\\nsamples = 12\\nvalue = [0, 12]'),\n",
       " Text(0.8571428571428571, 0.6875, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]')]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tree(clf2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5666666666666667, 0.9375, 'x[1] <= 0.237\\ngini = 0.487\\nsamples = 50\\nvalue = [21.0, 29.0]'),\n",
       " Text(0.26666666666666666, 0.8125, 'x[0] <= -0.572\\ngini = 0.308\\nsamples = 21\\nvalue = [17, 4]'),\n",
       " Text(0.41666666666666663, 0.875, 'True  '),\n",
       " Text(0.2, 0.6875, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.3333333333333333, 0.6875, 'x[1] <= -0.047\\ngini = 0.255\\nsamples = 20\\nvalue = [17, 3]'),\n",
       " Text(0.2, 0.5625, 'x[1] <= -2.229\\ngini = 0.124\\nsamples = 15\\nvalue = [14, 1]'),\n",
       " Text(0.13333333333333333, 0.4375, 'x[1] <= -2.344\\ngini = 0.375\\nsamples = 4\\nvalue = [3, 1]'),\n",
       " Text(0.06666666666666667, 0.3125, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
       " Text(0.2, 0.3125, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.26666666666666666, 0.4375, 'gini = 0.0\\nsamples = 11\\nvalue = [11, 0]'),\n",
       " Text(0.4666666666666667, 0.5625, 'x[1] <= 0.021\\ngini = 0.48\\nsamples = 5\\nvalue = [3, 2]'),\n",
       " Text(0.4, 0.4375, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.5333333333333333, 0.4375, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
       " Text(0.8666666666666667, 0.8125, 'x[1] <= 1.165\\ngini = 0.238\\nsamples = 29\\nvalue = [4, 25]'),\n",
       " Text(0.7166666666666667, 0.875, '  False'),\n",
       " Text(0.8, 0.6875, 'x[1] <= 1.126\\ngini = 0.36\\nsamples = 17\\nvalue = [4, 13]'),\n",
       " Text(0.7333333333333333, 0.5625, 'x[1] <= 0.766\\ngini = 0.305\\nsamples = 16\\nvalue = [3, 13]'),\n",
       " Text(0.6666666666666666, 0.4375, 'gini = 0.0\\nsamples = 6\\nvalue = [0, 6]'),\n",
       " Text(0.8, 0.4375, 'x[1] <= 0.871\\ngini = 0.42\\nsamples = 10\\nvalue = [3, 7]'),\n",
       " Text(0.7333333333333333, 0.3125, 'x[1] <= 0.832\\ngini = 0.48\\nsamples = 5\\nvalue = [3, 2]'),\n",
       " Text(0.6666666666666666, 0.1875, 'x[1] <= 0.798\\ngini = 0.444\\nsamples = 3\\nvalue = [1, 2]'),\n",
       " Text(0.6, 0.0625, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.7333333333333333, 0.0625, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.8, 0.1875, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
       " Text(0.8666666666666667, 0.3125, 'gini = 0.0\\nsamples = 5\\nvalue = [0, 5]'),\n",
       " Text(0.8666666666666667, 0.5625, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.9333333333333333, 0.6875, 'gini = 0.0\\nsamples = 12\\nvalue = [0, 12]')]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tree(clf3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\akkan\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf1.predict(np.array([-1.042498,\t-0.064323]).reshape(1,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\akkan\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf2.predict(np.array([-1.042498,\t-0.064323]).reshape(1,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\akkan\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf3.predict(np.array([-1.042498,\t-0.064323]).reshape(1,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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