MSE(均方误差)、RMSE (均方根误差)、MAE (平均绝对误差)
1、MSE(均方误差)(Mean Square Error)
MSE是真实值与预测值的差值的平方然后求和平均。
范围[0,+∞),当预测值与真实值完全相同时为0,误差越大,该值越大。
import numpy as npfrom sklearn import metricsy_true = np.array([1.0, 5.0, 4.0, 3.0, 2.0, 5.0, -3.0])y_pred = np.array([1.0, 4.5, 3.5, 5.0, 8.0, 4.5, 1.0])print(metrics.mean_squared_error(y_true, y_pred)) # 8.107142857142858
2、RMSE (均方根误差)(Root Mean Square Error)
import numpy as npfrom sklearn import metricsy_true = np.array([1.0, 5.0, 4.0, 3.0, 2.0, 5.0, -3.0])y_pred = np.array([1.0, 4.5, 3.5, 5.0, 8.0, 4.5, 1.0])print(np.sqrt(metrics.mean_squared_error(y_true, y_pred)))
3、MAE (平均绝对误差)(Mean Absolute Error)
import numpy as npfrom sklearn import metricsy_true = np.array([1.0, 5.0, 4.0, 3.0, 2.0, 5.0, -3.0])y_pred = np.array([1.0, 4.5, 3.5, 5.0, 8.0, 4.5, 1.0])print(metrics.mean_absolute_error(y_true, y_pred))
4、转载自
MSE(均方误差)、RMSE (均方根误差)、MAE (平均绝对误差)
【转载】MSE(均方误差) RMSE (均方根误差) MAE (平均绝对误差)- 机器学习 - 线性回归之模型评估