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Adding prediction intervals for GradientBoostingRegressor and review RandomForest prediction intervals - https://alice.its.cern.ch/jira/browse/ATO-459 #67
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In order to include new regression, classifiers - MLpipeline code to be restructured Current version (TO BE deprecated)
New version - to be implemented
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Reference - GradientBoostingRegressor https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.htmlquantiles obtained during training time - using appropriate cost function
Other Referenceshttps://towardsdatascience.com/regression-prediction-intervals-with-xgboost-428e0a018b |
Deep quantile regression:based on the cost function discussed in the:
describe scalar version - one quantile per Neural network. Quantile vector implementation in Jupyter notebook: |
Quantile regression interface:In general - quantiles should be defined before fitting (not needed in the Scikit -garden - but skgradern not supported anymore) BDTs and neural nets should be constructed knowing which quantiles are needed
Proposed interface:
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GradientBoostingRegressor wrapper should be added to the list of wrappers i RootInteractive:
https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html
https://medium.com/@qucit/a-simple-technique-to-estimate-prediction-intervals-for-any-regression-model-2dd73f630bcb
To be integrated in similar way also QuantileRegressionForest -clone
https://scikit-garden.github.io/examples/QuantileRegressionForests/
https://jmlr.org/papers/volume7/meinshausen06a/meinshausen06a.pdf
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