[ML] Stratified cross validation split for classification #54087
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benwtrent
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...elasticsearch/xpack/ml/dataframe/process/crossvalidation/CrossValidationSplitterFactory.java
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As classification now works for multiple classes, randomly picking training/test data frame rows is not good enough. This commit introduces a stratified cross validation splitter that maintains the proportion of the each class in the dataset in the sample that is used for training the model.
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| @@ -324,7 +324,8 @@ private void refreshIndices(String jobId) { | |||
| ); | |||
| refreshRequest.indicesOptions(IndicesOptions.lenientExpandOpen()); | |||
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| LOGGER.debug("[{}] Refreshing indices {}", jobId, Arrays.toString(refreshRequest.indices())); | |||
| LOGGER.debug(() -> new ParameterizedMessage("[{}] Refreshing indices {}", | |||
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@benwtrent I forgot to address your comment before so I squeezed this one here
przemekwitek
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dimitris-athanasiou
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… (#54104) As classification now works for multiple classes, randomly picking training/test data frame rows is not good enough. This commit introduces a stratified cross validation splitter that maintains the proportion of the each class in the dataset in the sample that is used for training the model. Backport of #54087
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As classification now works for multiple classes, randomly
picking training/test data frame rows is not good enough.
This commit introduces a stratified cross validation splitter
that maintains the proportion of the each class in the dataset
in the sample that is used for training the model.