classifier_utils.py 文件源码

def run_predict_random_forest(X_train,Y_train,X_test,Y_test, n_estimators=30, max_features=500, show_mistakes=False):
    forest = RandomForestClassifier(n_estimators=10, max_features=20, max_depth=10) 
    clf = SKClassifier(forest)
    forest_fit = clf.fit(X_train, Y_train)
    pred = forest_fit.predict(X_test)
    print('\n Random forest 0-1 error.  \n Train: ',zero_one_score(Y_train, forest_fit.predict(X_train)), '\n Test: ', 
      zero_one_score(Y_test, pred))


    met = clf.metrics(X_test,Y_test)
    if show_mistakes:
        mis = clf.show_mistakes(X_test,Y_test,10)
    print('Metrics:', met)
    return clf