I have a dataset and I want to train my model on that data. After training, I need to know the features that are major contributors in the classification for a SVM classifier.
There is something called feature importance for forest algorithms, is there anything similar?
Yes, there is attribute coef_
for SVM classifier but it only works for SVM with linear kernel. For other kernels it is not possible because data are transformed by kernel method to another space, which is not related to input space, check the explanation.
from matplotlib import pyplot as plt
from sklearn import svm
def f_importances(coef, names):
imp = coef
imp,names = zip(*sorted(zip(imp,names)))
plt.barh(range(len(names)), imp, align='center')
plt.yticks(range(len(names)), names)
plt.show()
features_names = ['input1', 'input2']
svm = svm.SVC(kernel='linear')
svm.fit(X, Y)
f_importances(svm.coef_, features_names)