为解决此问题，考虑使用对数RMSE（logarithmic RMSE）。先取对数操作，可以稍微缓解大值误差对整体评估的影响。RMSE在预测值分布固定时，评估结果较为合理。然而，对数转换后的RMSE同样存在适用范围限制，其效果依赖于数据特性。
mean_squared_logarithmic_error(y_true, y_pred) assert loss.shape == (2,) y_true = np.maximum(y_true, 1e-7) y_pred = np.maximum(y_pred, 1e-7) assert np.allclose( loss.numpy(), np.mean( np.square(np.log(y_true + 1.) - np.log(y_pred + 1.)), axis=-1))...
假如真实值为1000，若果预测值是600，那么RMSE=400， RMSLE=0.510 假如真实值为1000，若预测结果为1400...
RMSE就会很大。 相应的，如果另外一个比较差的算法对这一个大的值准确一些，但是很多小的值都有偏差，...
Root Mean Squared Logarithmic Error Lossytrue
any(): raise ValueError( "Root Mean Squared Logarithmic Error cannot be used when " "targets contain negative values." ) However, the actual calculations behind these errors are valid for values of y_true & y_pred larger than -1, so any values in y_true or y_pred that are in the ...
Kaggle calls this "[root] mean squared logarithmic error", not "[root] mean squared log error" which sounds like it's a function of the log of the error. I think this is an important distinction. I'm not sure if you need to rename the function and scorer to reflect this, but at ...
The root mean squared logarithmic error (RMSLE). RMSLE=1n∑i=1n(log(yiˆ+1)−log(yi+1))2 6.6.4Mean absolute percent error The mean absolute percent error (MAPE) is theamount of the accuracy of a prediction. It measures the size of the error (Fig. 6.5;Table 6.1). ...
Prediction errors were evaluated by determining theroot mean squareserrors of calibration (RMSEC),root mean squareserror of cross validation (RMSECV), and RMSEP. Busk et al [13] conducted a trial in Denmark and reported that the AutoFom I prediction of total LMP yielded a residualroot mean squ...
pi2+1)−log(ai+1))2 所以欠预测的loss更大。这里公式的+1实际问题中为了防止log(0)的问题。