categorical_nb.md

Categorical Naive Bayes

Categorical Naive Bayes model.

Categorical Naive Bayes computes likelihood by : occurences of instance in current label divided by total occurences of instance

It computes the prior probability by : occurence of current class divided by size of training array

And finally prior*likelihood gives the probabilty of class according to which output class is predicted.

Parameters

Name	Definition	Type
xTrain	The training set containing the features	vector<vector<T>>
yTrain	The set containing the class correspoding to respective xTrain instance	vector<string
xTest	The sample set , whose class will be predicted	vector<T>

Methods

Name	Definition	Return value
fit(vector<vector<T>> xTrain,vector<T> yTrain)	fit the class instance with the training data	NULL
predict(vector<T> xTest	predict the label for xTest vector of features	string

Example

cpp
std::vector <std::vector<std::string>> xTrain = {{"fifa", "yes", "no",  "no"},
                                                     {"fifa", "no",  "yes", "no"},
                                                     {"fifa", "no",  "no",  "yes"},
                                                     {"cc",   "no",  "no",  "yes"},
                                                     {"fifa", "yes", "yes", "yes"},
                                                     {"cc",   "yes", "yes", "yes"},
                                                     {"cc",   "no",  "no",  "yes"},
                                                     {"cc",   "yes", "no",  "no"}};
std::vector <std::string> yTrain = {"m", "m", "m", "m", "f", "f", "f", "f"};
std::vector <std::string> xTest = {"fifa", "no", "yes", "yes"};
categoricalNB<std::string> classifier;
classifier.fit(xTrain,yTrain);
std::cout<<classifier.predict(xTest);