在 jupyter notebook 中运行时出错

Question

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

Answer 1

It looks like you are making the param an array with a single dictionary inside. param needs to be just a dictionary:

EDIT : Looking into this further, as mentioned by @DzDev, passing an array containing a single dictionary is also a valid way to pass in parameters.

It appears that your issue is that you are mixing the concepts of two different types of estimators. You are passing in the parameters for svm.SVC but are sending in a DecisionTreeClassifier estimator. So it turns out the error is exactly as it says, 'C' is not a valid parameter. You should update to either using a svm.SVC estimator or updates your parameters to be correct for the DecisionTreeClassifier .