[ML] Set the class assignment probability threshold to maximise minimum recall #926
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… class recall by default
tveasey
changed the title
…ight change in the future
valeriy42
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Jan 14, 2020
| //! Set whether to try and balance within class accuracy. For classification | ||
| //! this reweights examples so approximately the same total loss is assigned | ||
| //! to every class. | ||
| CBoostedTreeFactory& balanceClassTrainingLoss(bool balance); |
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This change was short-lived ;)
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Indeed. I tested with and without this option and it didn't significantly alter results now that the estimated probability at which to assign to each class is floating. I still think there is mileage in getting balancing classes better, possibly by oversampling (undersampling) the minority (majority) class when downsampling for each tree, but haven't been able to get anything to work appreciably better, so just keeping it simple for the time being.
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| CSolvers::maximize(0.0, 1.0, minRecall(0.0), minRecall(1.0), minRecall, | ||
| 1e-3, maxIterations, threshold, minRecallAtThreshold); | ||
| LOG_TRACE(<< "threshold = " << threshold |
valeriy42
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Rather than using a fixed threshold on the P(class 1), this switches to optionally supporting two strategies for assigning class labels:
The default choice avoids pathologies for very imbalanced training data, where we can essentially assign all values to one class if we seek to maximise overall accuracy.
(We also need to introduce this threshold into the inference model schema. This needs support in the Java inference code to be merged first, and it will be made in a separate PR.)