RMSLE惩罚欠预测大于过预测。我是看bike sharing demand问题见到这个指标的，结合实际考虑某些情况下会存在...
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。
再求RMSE，这个过程就是RMSLE。对低估值（under-predicted）的判罚明显多于估值过高(over-predicted)的情况...
The :func:`mean_squared_log_error` function computes a risk metric corresponding to the expected value of the logarithmic squared (quadratic) error or loss. Member jnothman Nov 6, 2016 I think you want "squared logarithmic" rather than "logarithmic squared". Author kdexd Nov 6, 2016 Oo...
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). ...
where\(\varvec{x}_{i}\)are the original image pixels,\(\varvec{y}_{i}\)are the restored samples and the number of image pixels isn. In order to better express the ability of filtering out impulses, arelaxedMean Squared Error measure (\(\text {MSE}_{\text {R}}\)) is used. ...
Finally, accounting for the remaining control variables leads to a loss of 111 additional firms. Hence, our final dataset consists of firms covering the fiscal years 1994 to 2019 and 107 four-digit North American Industry Classification System (NAICS) industries resulting in 6,389 firm-year ...
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))...
求助：用xgboost的eval_metric只有rmse，没有rmsle。怎么自定义？
Root Mean Squared Logarithmic Error Lossytrue