@@ -0,0 +1,792 @@
+import asl%0A%0Afrom sklearn.cross_validation import train_test_split%0Afrom sklearn.grid_search import GridSearchCV%0Afrom sklearn.metrics import classification_report%0Afrom sklearn.svm import SVC%0A%0An_samples = len(asl.data)%0AX = asl.data%0Ay = asl.target%0A%0AX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5,%0A        random_state=0)%0A%0A#tuned_parameters = %5B%7B'kernel': %5B'rbf'%5D, 'gamma': %5B1e-3, 1e-4%5D, 'C': %5B1, 10, 100,%0A#    1000%5D%7D, %7B'kernel': %5B'linear'%5D, 'C': %5B1, 10, 100, 1000%5D%7D%5D%0A%0Atuned_parameters = %5B%7B'kernel': %5B'rbf'%5D, 'gamma': %5B0.001, 0.00055, 0.0001%5D, 'C':%0A    %5B10, 50, 100%5D%7D%5D%0A%0Afor score in %5B'precision', 'recall'%5D:%0A    clf = GridSearchCV(SVC(C=1), tuned_parameters, cv=5, scoring='%25s_weighted' %25%0A            score)%0A    clf.fit(X_train, y_train)%0A%0A    print score, clf.best_params_%0A

Add script to find parameters for SVC