Prediction intervals are commonly used for linear models but are often underused for random forests. Leveraging the fact that a random forest can provide a conditional distribution instead of just the conditional mean makes prediction intervals relatively straightforward to use in this context.
Read it!An in-depth analysis of why certain types of tests break more frequently than others, along with suggestions for creating more robust pipeline tests.
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