如何在H2O随机森林或其他二进制分类器中指定肯定类？ [英] How do I specify the positive class in an H2O random forest or other binary classifier?

问题描述

我正在使用Python在H2O中构建二进制分类模型。我的 y值是好和不好。我需要使用ok =否定类别= 0和bad =肯定类别= 1来计算指标。但是，我看不到有任何方法可以在H2O中进行设置。例如，以下是预测和混淆矩阵的输出：

I am building a binary classification model in H2O with Python. My 'y' values are 'ok' and 'bad'. I need the metrics to be computed with ok = negative class = 0 and bad = positive class = 1. However, I do not see any way to set this in H2O. For example here is the output of the predictions and confusion matrix:

confusion matrix
        bad    ok  Error              Rate
  bad  3859   631 0.1405    (631.0/4490.0)
   ok   477  1069 0.3085    (477.0/1546.0)
Total  4336  1700 0.1836   (1108.0/6036.0)


>>> predictions.head(10)
  predict       bad        ok
0     bad  0.100604  0.899396
1     bad  0.100604  0.899396
2     bad  0.112232  0.887768
3      ok  0.068917  0.931083
4      ok  0.089706  0.910294
5      ok  0.089706  0.910294
6      ok  0.089706  0.910294
7     bad  0.126182  0.873818
8     bad  0.126182  0.873818
9      ok  0.092306  0.907694

H2O似乎是根据标签中的字母顺序任意确定的。如果我将标签更改为 ok和 sad，这就是我得到的：

H2O seems to arbitrarily decide based on alphabetical order among the labels. If I change the labels to 'ok' and 'sad' here is what I get:

confusion matrix
         ok   sad  Error             Rate
   ok   798   732 0.4784   (732.0/1530.0)
  sad   211  4381 0.0459   (211.0/4592.0)
Total  1009  5113 0.1540   (943.0/6122.0)


>>> predictions.head(10)
  predict        ok       sad
0     sad  0.215206  0.784794
1     sad  0.211073  0.788927
2     sad  0.211073  0.788927
3      ok  0.236190  0.763810
4      ok  0.241641  0.758359
5      ok  0.241641  0.758359
6      ok  0.236099  0.763901
7     sad  0.162072  0.837928
8     sad  0.162072  0.837928
9     sad  0.206146  0.793854

必须有一种方法可以以编程方式设置哪个标签是正类别，哪个标签是负类别？

There must be a way to programmatically set which label is the positive class and which is the negative class?

推荐答案

如果 df 是您的H2O框架，则 df ['y'] = df ['y']。relevel（'ok'）应将'ok'设置为0级。请参见 http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/frame .html＃h2o.frame.H2OFrame.relevel

If df is your H2O Frame then df['y'] = df['y'].relevel('ok') should set 'ok' to level 0. See http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/frame.html#h2o.frame.H2OFrame.relevel

这篇关于如何在H2O随机森林或其他二进制分类器中指定肯定类？的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！