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!25 best practices to mitigate technical debt in machine learning.
Read it!A journey starting from the standard formulation of linear regression, moving on to the probabilistic approach, and then progressing to Bayesian linear regression.
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