```
import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
import numpy as np
from sklearn import linear_model
import matplotlib.pyplot as plt
%matplotlib inline
def plot_classifier(classifier, X, y):
# define ranges to plot the figure
x_min, x_max = min(X[:, 0]) - 1.0, max(X[:, 0]) + 1.0
y_min, y_max = min(X[:, 1]) - 1.0, max(X[:, 1]) + 1.0
# denotes the step size that will be used in the mesh grid
step_size = 0.01
# define the mesh grid
x_values, y_values = np.meshgrid(np.arange(x_min, x_max, step_size), np.arange(y_min, y_max, step_size))
# compute the classifier output
mesh_output = classifier.predict(np.c_[x_values.ravel(), y_values.ravel()])
# reshape the array
mesh_output = mesh_output.reshape(x_values.shape)
# Plot the output using a colored plot
plt.figure()
# choose a color scheme you can find all the options
# here: http://matplotlib.org/examples/color/colormaps_reference.html
plt.pcolormesh(x_values, y_values, mesh_output, cmap=plt.cm.gray)
# Overlay the training points on the plot
plt.scatter(X[:, 0], X[:, 1], c=y, s=80, edgecolors='black', linewidth=1, cmap=plt.cm.Paired)
# specify the boundaries of the figure
plt.xlim(x_values.min(), x_values.max())
plt.ylim(y_values.min(), y_values.max())
# specify the ticks on the X and Y axes
plt.xticks((np.arange(int(min(X[:, 0])-1), int(max(X[:, 0])+1), 1.0)))
plt.yticks((np.arange(int(min(X[:, 1])-1), int(max(X[:, 1])+1), 1.0)))
plt.show()
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)
classifier_gaussiannb = GaussianNB()
classifier_gaussiannb.fit(X, y)
y_pred = classifier_gaussiannb.predict(X)
# compute accuracy of the classifier
accuracy = 100.0 * (y == y_pred).sum() / X.shape[0]
print("Accuracy of the classifier =", round(accuracy, 2), "%")
plot_classifier(classifier_gaussiannb, X, y)
###############################################
# Train test split
from sklearn import cross_validation
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5)
classifier_gaussiannb_new = GaussianNB()
classifier_gaussiannb_new.fit(X_train, y_train)
y_test_pred = classifier_gaussiannb_new.predict(X_test)
# compute accuracy of the classifier
accuracy = 100.0 * (y_test == y_test_pred).sum() / X_test.shape[0]
print("Accuracy of the classifier =", round(accuracy, 2), "%")
plot_classifier(classifier_gaussiannb_new, X_test, y_test)
###############################################
# Cross validation and scoring functions
num_validations = 5
accuracy = cross_validation.cross_val_score(classifier_gaussiannb,
X, y, scoring='accuracy', cv=num_validations)
print("Accuracy: " + str(round(100*accuracy.mean(), 2)) + "%")
f1 = cross_validation.cross_val_score(classifier_gaussiannb,
X, y, scoring='f1_weighted', cv=num_validations)
print("F1: " + str(round(100*f1.mean(), 2)) + "%")
precision = cross_validation.cross_val_score(classifier_gaussiannb,
X, y, scoring='precision_weighted', cv=num_validations)
print("Precision: " + str(round(100*precision.mean(), 2)) + "%")
recall = cross_validation.cross_val_score(classifier_gaussiannb,
X, y, scoring='recall_weighted', cv=num_validations)
print("Recall: " + str(round(100*recall.mean(), 2)) + "%")
```