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. SVMs are used for classification and regression.
SVM do that by finding a hyperplane between two classes of data which separates both classes best.
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”
C
Penalty parameter C of the error term
Kernels and the Kernel trick
RBF
Linear Digression Podcast on Kernel Trick
