I am new to machine learning and try to use scikit-learn(sklearn) to deal with a classification problem. Both DecisionTree and SVM can train a classifier for this problem.
sklearn.svm.SVC to fit the same training data(about 500,000 entries with 50 features per entry). The RandomForestClassifier comes out with a classifier in about one minute. The SVC uses more than 24 hours and still keeps running.
Why does the SVC perform so inefficiently? Is the data set too big for SVC? Is SVC improper for such problem?