Random Forest Cheat Sheet Condenses Key Decisions Into One Reference Page

A comprehensive one-page reference guide for Random Forest models has been published, covering the core tuning decisions practitioners face when building and debugging these ensembles. The guide explains three randomness controls — bootstrap sampling, column subsampling via max_features, and random threshold selection in Extra Trees — each of which trades per-tree accuracy for lower inter-tree correlation. It provides separate hyperparameter starting ranges for classification and regression tasks, noting that max_features is the single most impactful parameter and its optimal value differs significantly between the two problem types. The cheat sheet also addresses feature importance methods, warning that the default MDI measure is a training statistic that inflates scores for high-cardinality columns, and recommends permutation importance on held-out data instead. Finally, it compares Bagging, Random Forest, and Extra Trees with measured accuracy figures, and lists common production failure symptoms — such as grouped rows causing OOB score inflation — alongside recommended fixes.