def svm_cv(data, data_target): X_train, X_test, y_train, y_test = cross_validation.train_test_split(data, data_target) print "*" * 79 print "Training..." # selector = SelectFdr(chi2) selector = SelectFdr(f_classif) selector.fit(X_train, y_train) clf = svm.SVC(kernel='linear', probability=True) clf.fit(selector.transform(X_train), y_train) print "Testing..." pred = clf.predict(selector.transform(X_test)) probs = pred.predict_proba(selector.transfrom(X_test)) accuracy_score = metrics.accuracy_score(y_test, pred) classification_report = metrics.classification_report(y_test, pred) support = selector.get_support() print support print accuracy_score print classification_report precision, recall, thresholds = precision_recall_curve(y_test, probs[:, 1])