Lesson 3: Support Vector Machines

The term Support Vector Machines or SVM is a bit misleading. It is just a name for a very clever algorithm invented by two Russians. in the 1960s. SVM are used for classification and regression.

print("Start training")
t0 = time()
clf = svm.SVC(kernel="linear")
clf.fit(features_train, labels_train)
print("training time:", round(time() - t0, 3), "s")

print("start predicting")
t0 = time()
prediction = clf.predict(features_test)
print("predict time:", round(time() - t0, 3), "s")

# accuracy
print("Calculating accuracy")
accuracy = accuracy_score(labels_test, prediction)
print("Accuracy calculated, and the accuracy is", accuracy)

When timing the training of the SVC, it’s astonishing how long it takes: around 2.5 minutes at 98.4% accuracy.

As an alternative You can use:

clf = LinearSVC(loss='hinge')

It gets you a result in 0.3 seconds with the same accuracy.

What’s the difference?

Parameter tuning

with the initial SVC we can play around with the parameters “C” and “kernel”



Ingo’s Deep Dive


SVM Siraj Raval


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