Splitting the dataset for training and testing

Splitting helps to partition the dataset into training and testing sequences.

How to do it...

1. Add the following code fragment into the same Python file:

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

2. Allocate 75% of data for training and 25% of data for testing:

a_training, a_testing, b_training, b_testing = cross_validation.train_test_split(a, b, test_size=0.25, random_state=5)
classification_gaussiannb_new = GaussianNB()
classification_gaussiannb_new.fit(a_training, b_training)

3. Evaluate the classifier performance on test data:

b_test_pred = classification_gaussiannb_new.predict(a_testing)

4. Compute the accuracy of the classifier system:

correctness...