Error: Invalid parameter C for estimator DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None, max_features=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, min_samples_leaf=1, min_samples_split=2, min_weight_fraction_leaf=0.0, presort=False, random_state=None, splitter='best'). Check the list of available parameters with `estimator.get_params().keys()`. Code: def train(X_train,y_train,X_test): # Scaling features X_train=preprocessing.scale(X_train) X_test=preprocessing.scale(X_test) Cs = 10.0 ** np.arange(-2,3,.5) gammas = 10.0 ** np.arange(-2,3,.5) param = [{'gamma': gammas, 'C': Cs}] skf = StratifiedKFold(n_splits=5) skf.get_n_splits(X_train, y_train) cvk = skf classifier = DecisionTreeClassifier() clf = GridSearchCV(classifier,param_grid=param,cv=cvk) clf.fit(X_train,y_train) print("The best classifier is: ",clf.best_estimator_) clf.best_estimator_.fit(X_train,y_train) # Estimate score scores = model_selection.cross_val_score(clf.best_estimator_, X_train,y_train, cv=5) print (scores) print('Estimated score: %0.5f (+/- %0.5f)' % (scores.mean(), scores.std() / 2)) title = 'Learning Curves (SVM, rbf kernel, $\gamma=%.6f$)' %clf.best_estimator_.gamma plot_learning_curve(clf.best_estimator_, title, X_train, y_train, cv=5) plt.show() # Predict class y_pred = clf.best_estimator_.predict(X_test) return y_test,y_pred Continue reading...
