RegressionRandomForest.py 文件源码

def parameterChoosing(self):
        #Set the parameters by cross-validation
        tuned_parameters = [{'max_depth': range(20,60),
                             'n_estimators': range(10,40),
                             'max_features': ['sqrt', 'log2', None]
                             }
                            ]

        clf = GridSearchCV(RandomForestRegressor(n_estimators=30), tuned_parameters, cv=5, scoring='mean_squared_error')
        clf.fit(self.X_train, self.y_train.ravel())

        print "Best parameters set found on development set:\n"
        print clf.best_params_

        print "Grid scores on development set:\n"
        for params, mean_score, scores in clf.grid_scores_:
            print "%0.3f (+/-%0.03f) for %r\n" % (mean_score, scores.std() * 2, params)

        print "MSE for test data set:\n"
        y_true, y_pred = self.y_test, clf.predict(self.X_test)
        print mean_squared_error(y_true, y_pred)