I'm using the following code for accuracy score calculation. Why is it so that the default configuration gives better result than GridSearch?

Default configuration

clf = svm.SVC(kernel='rbf', gamma='auto')               
clf.fit(x_train, y_train.values.ravel())                

y_train_pred = clf.predict(x_train)             
y_test_pred = clf.predict(x_test)               

print('Train set accuracy: '+'{}'.format(metrics.accuracy_score(y_train, y_train_pred)))                
print('Test set accuracy: '+'{}'.format(metrics.accuracy_score(y_test, y_test_pred)))   

Train set accuracy: 0.861101243339254
Test set accuracy: 0.8480113636363636

GridSearch configuration

param_grid = {'C': (0.001, 0.01, 0.1, 1, 10),
              'kernel': ('linear', 'poly', 'rbf', 'sigmoid'),
              'class_weight': ('balanced', None),
              'gamma' : ('scale', 'auto'),
              'shrinking': (True, False)}

grid_search = GridSearchCV(svm.SVC(gamma='scale'), param_grid, cv=5)
grid_results = grid_search.fit(x_train, y_train.values.ravel())


SVC(C=1, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False)
{'C': 1, 'class_weight': None, 'gamma': 'auto', 'kernel': 'rbf', 'shrinking': True}


2 Answers 2


Technically: Because grid search creates subsamples of the data repeatedly. That means the SVC is trained on 80% of x_train in each iteration and the results are the mean of predictions on the other 20%.

Theoretically: Because you conflate the questions of hyperparameter tuning (selection) and model performance estimation. GridSearch is used for selecting a combination of hyperparameters, performance estimation has not yet happened. The only comparison you should be making is between the parameter combinations within the CV itself (grid_results.cv_results). In my opinion, the reported CV train accuracy is within acceptable boundaries from non-CV training (meaning your SVC is able to extract a lot of generalization from subsamples). See e.g Cawley 2010

It would be interesting to see the reported performance on x_test on the retrained estimator grid_results.best_estimator_.predict(x_test), if it was kept separate from x_train, and how it is different from the first results.

  • $\begingroup$ @Tauno Indeed the winning model has the same parameters as the one you trained first. If you are interested in attempting to tune further consider values of C around 1. $\endgroup$
    – ludan
    Jun 30, 2019 at 9:58

Reported performance on x_test on the retrained estimator:

y_test_pred_GS = grid_results.best_estimator_.predict(x_test)
print('Accuracy after GridSearch: '+'{}'.format(metrics.accuracy_score(y_test, y_test_pred_GS))) 

Accuracy after GridSearch: 0.8480113636363636

So, I then interpret this as the default results are good enough and hyperparameter tuning can't make it better.


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