msesia/chr
Conformal Histogram Regression: efficient conformity scores for non-parametric regression problems
When you need to make predictions with a range, not just a single number, this tool helps you understand the uncertainty. It takes your existing machine learning model's predictions and transforms them into reliable prediction intervals, especially useful for data that isn't perfectly symmetrical or 'normal'. Data scientists and quantitative analysts can use this to get more robust estimates.
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Use this if you need to quantify the uncertainty of your non-parametric regression predictions with robust, conditionally valid prediction intervals.
Not ideal if you are looking for a new machine learning model to make point predictions, rather than a method to improve the uncertainty quantification of an existing one.
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Python
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Last pushed
Mar 26, 2022
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