Day #316 - SVM Classification Examples

	#https://scikit-learn.org/stable/modules/svm.html#classification
	#SVM
	#Classification, Outlier detection
	#Useful for high dimensional spaces
	#two class classification
	from sklearn import svm
	x = [[0,0],[1,1],[3,3],[5,5],[2,3]]
	y = [0,1,2,1,1]
	svmmodel = svm.SVC()
	svmmodel.fit(x,y)
	#property attributes of SVM
	print(svmmodel.support_vectors_)
	#indexes of support vectors
	print(svmmodel.support_)
	#number of support vectors for each class
	print(svmmodel.n_support_)
	#multi-class classification
	#SVC, NuSVC and LinearSVC are classes capable of performing multi-class classification on a dataset.
	#For unbalanced problems certain individual samples keywords class_weight and sample_weight can be used.
	svmweightmodel = svm.SVC(class_weight={0:0.8,1:0.1,2:0.1})
	svmweightmodel.fit(x,y)
	print('Default Prediction')
	print(svmmodel.predict([[1.,1.]]))
	print('Weight Prediction')
	print(svmweightmodel.predict([[1.,1.]]))
	#linear svc
	#LinearSVC implements “one-vs-the-rest” multi-class strategy
	linearsvcmodel = svm.LinearSVC()
	linearsvcmodel.fit(x,y)
	print('linearsvcmodel Prediction')
	print(linearsvcmodel.predict([[1.,1.]]))

view raw SVMExamples.py hosted with ❤ by GitHub

Happy Learning!!!