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...
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，...
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. ...
如果是有较大值的话 那么data就会有skew 在做模型之前就先log转换了不就行了 最后在算RMSE的时候在...
Due to the vast number of negatives (for a tail label), the loss function's minimum will not be achieved when the negatives are classified correctly with margin one as is the case with squared-hinge, but will in fact train a larger margin for the negatives. This suggests that by ...
继承自:Loss 用法 tf.keras.losses.MeanSquaredLogarithmicError( reduction=losses_utils.ReductionV2.AUTO, name='mean_squared_logarithmic_error' ) 参数 reduction 类型tf.keras.losses.Reduction适用于损失。默认值为AUTO.AUTO表示缩减选项将由使用上下文确定。对于几乎所有情况,这默认为SUM_OVER_BATCH_SIZE.当与tf...