Myujth

I split my dataset in two parts: training set and test set. For now just forget the test set and use the training set with the function GridSearchCV of the package sklearn.model_selection to search the best parameters for an SVM:

Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]
gammas = [0.001, 0.01, 0.1, 1]

# Set the parameters by cross-validation
param_grid = ['kernel': ['rbf'], 'gamma': gammas, 'C': Cs]

clf = GridSearchCV(svm.SVC(), param_grid = param_grid, cv=nfolds, verbose=1.)
clf.fit(x_train, labels)

after found my best C and gamma parameters, I create an SVM and I fit it with the training set (used before to search the best C and gamma):

model = svm.SVC(kernel='rbf', C = clf.best_params_['C'], gamma = clf.best_params_['gamma']) 
model.fit(x_train, y_train)

At this point I tried one thing, I used the predict() function of the GridSearchCV object and the one of the svm.SVC object:

predicted_label1 = model.predict(x_test)
predicted_label2 = clf.predict(x_test)

and then I used the classification_report(y_test, predicted_label) to valuate my two predicted_label vectors. In my mind I should obtain the same values but this not happens...Here my output:

 precision recall f1-score support

 0.0 0.24 0.97 0.39 357
 1.0 0.00 0.00 0.00 358
 2.0 0.00 0.00 0.00 357
 3.0 0.00 0.00 0.00 357

avg / total 0.06 0.24 0.10 1429

fine parametri
training set and test set saved
Create SVM classifier
 precision recall f1-score support

 0.0 0.70 0.63 0.66 357
 1.0 0.89 0.90 0.90 358
 2.0 0.89 0.94 0.91 357
 3.0 0.85 0.88 0.86 357

avg / total 0.83 0.84 0.83 1429

The first is from the GridSearchCV and the second from the SVM...
Is this normal?
What does GridSearchCV returns? Does it fit with the passed training set?