Building a Naive Bayes classifier

A Naive Bayes classifier is a supervised learning classifier that uses Bayes' theorem to build the model. Let's go ahead and build a Naïve Bayes classifier.

How to do it…

1. We will use naive_bayes.py that is provided to you as reference. Let's import a couple of things:

   from sklearn.naive_bayes import GaussianNB 
   from logistic_regression import plot_classifier

2. You were provided with a data_multivar.txt file. This contains data that we will use here. This contains comma-separated numerical data in each line. Let's load the data from this file:

   input_file = 'data_multivar.txt'
   
   X = []
   y = []
   with open(input_file, 'r') as f:
       for line in f.readlines():
           data = [float(x) for x in line.split(',')]
           X.append(data[:-1])
           y.append(data[-1]) 
   
   X = np.array(X)
   y = np.array(y)

   We have now loaded the input data into X and the labels into y.

3. Let's build the Naive Bayes classifier:

   classifier_gaussiannb = GaussianNB()
   classifier_gaussiannb.fit(X, y)
   y_pred...