当数据集中没有负值时显示具有负值的数据点的散点图

Question

此线性回归预测模型存在问题。当数据集中不存在负值时，散点图显示的数据点为负值。我已经检查了形状和最小值，图表不应显示这些负值，但我无法弄清楚为什么散点图表明它们存在。

指标定义代码

def evaluate_model(y_test,price_pred):
gradient = price_linear.coef_
intercept = price_linear.intercept_

mile_mae = mean_absolute_error(y_test, price_pred)
mile_mse = mean_squared_error(y_test, price_pred)
mile_rmse=np.sqrt(mile_mse)
mile_r2 = r2_score(y_test, price_pred)

print(f'Gradient: {gradient} \n Intercept: {intercept}')

print(f'\n Mean absolute error: {mile_mae} \n Mean squared error: {mile_mse} \n Root 
mean squared error: {mile_rmse} \n Coefficient of determination: {mile_r2}')

线性回归模型的代码

numerical_inputs = ['Mileage', 'Year of manufacture', 'Engine size']

x = df[numerical_inputs]
y = df['Price']

#splitting of the data 
x_num_train, x_num_test, y_price_train, y_price_test = train_test_split(x, y, test_size 
= 0.2, random_state = 42)

#scaling the numerical data 
scale = StandardScaler()

#fitting only to train data to prevent data leakage 
scale.fit(x_num_train)
num_train_scaled = scale.transform(x_num_train)
num_test_scaled = scale.transform(x_num_test)

multi_price_linear = LinearRegression() 

multi_price_linear.fit(num_train_scaled, y_price_train)

multi_price_pred = multi_price_linear.predict(num_test_scaled)

evaluate_model(y_price_test, multi_price_pred)


#plt.show()
plt.figure(figsize=(14,8))
plt.scatter(y_price_test, multi_price_pred, alpha = 0.6)
plt.plot([min(y_price_test), max(y_price_test)], [min(y_price_test), 
max(y_price_test)], color = 'red')
plt.ylabel('Actual Price')
plt.xlabel('Predicted Price')
plt.title('Predicted Price vs Actual Price')
plt.show()

输出如下：

渐变：[-2720.41736808 9520.41488938 6594.02448017] 拦截：13854.628699999997

平均绝对误差：6091.458141656242 均方误差：89158615.76017143 均方根误差：9442.38400829851 决定系数：0.671456306417368

这是散点图所指示内容的图像：

如果这表明数据或代码存在问题，我不想限制图表显示负值。谢谢！

Answer 1

答案是您不小心切换了轴标签。您的预测值绘制在 Y 轴上，您的实际值绘制在 X 轴上。