sklearnmetrics.log_loss为正vs.'neg_log_loss'为负 [英] sklearn metrics.log_loss is positive vs. scoring 'neg_log_loss' is negative

问题描述

确保我做对了:

如果我们使用 sklearn.metrics.log_loss 独立的，即log_loss(y_true，y_pred)，它会产生正的分数-分数越小，性能越好.

If we use sklearn.metrics.log_loss standalone, i.e. log_loss(y_true,y_pred), it generates a positive score -- the smaller the score, the better the performance.

但是，如果我们使用"neg_log_loss" 作为"cross_val_score"中的计分方案，分数为负-分数越大，性能越好.

However, if we use 'neg_log_loss' as a scoring scheme as in 'cross_val_score", the score is negative -- the bigger the score, the better the performance.

这是由于计分方案的构建与其他计分方案保持一致.一般而言，越高越好，所以我们否定通常的 log_loss 以符合趋势.这样做完全是出于此目的.这种理解正确吗?

And this is due to the scoring scheme is built to be consistent with other scoring schemes. Since generally, the higher the better, we negate usual log_loss to be consistent with the trend. And it is done so solely for that purpose. Is this understanding correct?

[背景:metric.log_loss的得分为正，'neg_los_loss'的得分为负，两者均参考同一文档页面.]

推荐答案

sklearn.metrics.log_loss是通常定义的错误度量的实现，并且大多数错误度量都是正数.在这种情况下，与诸如最大化的准确性之类的度量相反，它是通常最小化的度量(例如，作为回归的均方误差).

The sklearn.metrics.log_loss is an implementation of the error metric as typically defined, and which is as most error metrics a positive number. In this case, it is a metric which is generally minimized (e.g. as mean squared error for regression), in contrast to metrics such as accuracy which is maximized.

因此neg_log_loss是创建实用程序值的技术，它允许优化sklearn的函数和类以最大化此实用程序，而不必更改每个度量的函数行为(例如，包括名为cross_val_score，GridSearchCV，RandomizedSearchCV等).

The neg_log_loss is hence a technicality to create a utility value, which allows optimizing functions and classes of sklearn to maximize this utility without having to change the function's behavior for each metric (such include for instance named cross_val_score, GridSearchCV, RandomizedSearchCV, and others).

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