For a svm classifier, how do I get the probability estimates (probability of a certain sample X, if it s below 0.5 for example it will be classified as 0 and beyond as 1) ? Python has function that give me the distance of sample X to the decision boundary (hyperplane). How can I convert this distance (has positiv an negativ values) to get the probabilities in a range (0,1) ? I have to plot a precision_recall function without using the scikit_learn function plot_ precison_recall
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I think this stack overflow post answers the same question: https://stackoverflow.com/questions/49507066/predict-probabilities-using-svm – Ayush Goel Dec 04 '20 at 06:21
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Thank you very much¦ Have a nice day¦ – Beatrice-Daniela Bucur Dec 04 '20 at 06:25
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Does this answer your question? [Predict probabilities using SVM](https://stackoverflow.com/questions/49507066/predict-probabilities-using-svm) – Stevy Dec 04 '20 at 09:13