Python Machine Learning Cookbook - Second Edition

How to do it...

In this recipe, we will learn how to build a linear classifier using SVMs:

1. We need to split our dataset into training and testing datasets. Add the following lines to the same Python file:

# Train test split and SVM training 
from sklearn import cross_validation 
from sklearn.svm import SVC 
 
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.25, random_state=5) 

2. Let's initialize the SVM object using a linear kernel. Add the following lines to the file:

params = {'kernel': 'linear'} 
classifier = SVC(**params, gamma='auto') 

3. We are now ready to train the linear SVM classifier:

classifier.fit(X_train, y_train) 

4. We can now see how the classifier performs:

utilities.plot_classifier(classifier, ...