onehot.py 文件源码

def GDBT_regression(X=train_df_munged,Y=label_df['SalePrice']):
    est = GradientBoostingRegressor(n_estimators=50,max_depth=3,learning_rate=0.1)
    X_train,X_test,Y_train,Y_test = train_test_split(X,Y,test_size=0.3,random_state=0)
    est.fit(X_train,Y_train)
    y_train_pred = est.predict(X_test)
    plt.scatter(y_train_pred,y_train_pred - Y_test,c = 'blue',marker='s', label='error on training data')

    plt.title("Linear regression with  GDBT")
    plt.xlabel("Predicted values")
    plt.ylabel("Residuals")
    plt.legend(loc="upper left")
    plt.hlines(y=0, xmin=10.5, xmax=13.5, color="red")
    plt.show()
    # Plot predictions
    plt.scatter(Y_test, y_train_pred, c="blue", marker="s", label="Training data")

    plt.title("Linear regression with  GDBT")
    plt.xlabel("Predicted values")
    plt.ylabel("Real values")
    plt.legend(loc="upper left")
    plt.plot([10.5, 13.5], [10.5, 13.5], c="red")
    plt.show()
    print('rmse value:',rmse(Y_test,y_train_pred))

    return est