回归模型指标：MSE 、 RMSE、 MAE、R2

sklearn调用

# 测试集标签预测
y_predict = lin_reg.predict(X_test)

# 衡量线性回归的MSE 、 RMSE、 MAE、r2
from math import sqrt
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
print("mean_absolute_error:", mean_absolute_error(y_test, y_predict))
print("mean_squared_error:", mean_squared_error(y_test, y_predict))
print("rmse:", sqrt(mean_squared_error(y_test, y_predict)))
print("r2 score:", r2_score(y_test, y_predict))

原生实现

# 测试集标签预测
y_predict = lin_reg.predict(X_test)
# 衡量线性回归的MSE 、 RMSE、 MAE
mse = np.sum((y_test - y_predict) ** 2) / len(y_test)
rmse = sqrt(mse)
mae = np.sum(np.absolute(y_test - y_predict)) / len(y_test)
r2 = 1-mse/ np.var(y_test)
print("mse:",mse,"  rmse:",rmse,"  mae:",mae,"  r2:",r2)

相关公式

• MSE
  
• RMSE
  
• MAE
  
• R2