GridSearchCV warm-up
In the video we saw that increasing the RBF kernel hyperparameter gamma increases training accuracy. In this exercise we'll search for the gamma that maximizes cross-validation accuracy using scikit-learn's GridSearchCV. A binary version of the handwritten digits dataset, in which you're just trying to predict whether or not an image is a "2", is already loaded into the variables X and y.
Diese Übung ist Teil des Kurses
Linear Classifiers in Python
Anleitung zur Übung
- Create a
GridSearchCVobject. - Call the
fit()method to select the best value ofgammabased on cross-validation accuracy.
Interaktive Übung
Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.
# Instantiate an RBF SVM
svm = SVC()
# Instantiate the GridSearchCV object and run the search
parameters = {'gamma':[0.00001, 0.0001, 0.001, 0.01, 0.1]}
searcher = GridSearchCV(svm, ____)
____.fit(____)
# Report the best parameters
print("Best CV params", searcher.best_params_)