1.3 Data visualization

In [5]:
plt.figure(figsize=(12,8))
plt.scatter(X0_train[0,:],X0_train[1,:], color = 'b', label = 'class 0 train')
plt.scatter(X1_train[0,:],X1_train[1,:], color = 'r',  label = 'class 1 train')
plt.scatter(X0_test[0,:],X0_test[1,:], color = 'LightBlue', label = 'class 0 test')
plt.scatter(X1_test[0,:],X1_test[1,:], color = 'Orange', label = 'class 1 test')
plt.xlabel('feature1')
plt.ylabel('feature2')
plt.legend()
plt.axis('equal')
plt.show()

1.4 Checking the shape of the input data

In [6]:
print('x_train:\t{}' .format(X_train.shape))
print('y_train:\t{}' .format(y_train.shape))
print('x_test:\t\t{}'.format(X_test.shape))
print('y_test:\t\t{}'.format(y_test.shape))

x_train:	(2000, 2)
y_train:	(1, 2000)
x_test:		(1000, 2)
y_test:		(1, 1000)