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i have a dataset in kaggle and i must compare different ML algorithms accuracy for a binary classification problem. the shape of data is (500000,18). but when i implement any kind of SVM like linear, rbf , poly ... the code wont stop even after a day and there is no error that i can figure out whats the problem. the data set addresss is : and my code is simple :

from sklearn.svm import SVC
clf = SVC(kernel='linear',C=0.001)

clf.fit(X_train, y_train)

i must mention that the accuracy isnt important i just must run different kernel for comparing the accuracies. is there any tips for running SVM for this dataset?

本文标签： pythonrunning a SVM classification wont stop on my datasetStack Overflow

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