Building a Naive Bayes classifier

A Naive Bayes classifier employs Bayes' theorem to construct a supervised model.

How to do it...

1. Import the following packages:

from sklearn.naive_bayes import GaussianNB
import numpy as np
import matplotlib.pyplot as plt

2. Use the following data file, which includes comma-separated arithmetical data:

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

3. Construct a Naive Bayes classifier:

classification_gaussiannb = GaussianNB()
classification_gaussiannb.fit(a, b)
b_pred = classification_gaussiannb.predict(a)

4. Calculate the accuracy of Naive Bayes:

correctness = 100.0 * (b == b_pred).sum() / a.shape[0]
print "correctness of the classification =", round(correctness, 2), "%"

5. Plot the classifier result:

def plot_classification(classification_gaussiannb, a , b):
  a_min, a_max = min(a[:, 0]) - 1.0,...