Random Forests

A random forest fits trees to randomized views of training data and considers subsets of features during split selection. Combining their predictions can reduce variance when trees are not perfectly correlated. Classification aggregates class evidence, while regression commonly averages responses. Bootstrap-based forests can use out-of-bag observations for an internal assessment, though that assessment inherits the sampling assumptions. Forests model nonlinear interactions but remain collections of axis-aligned partitions. Their competence profile differs from gradient boosting: trees are trained with randomization and aggregation rather than sequentially correcting an additive model's loss.

Prepare features with consistent semantics and select classification or regression settings suited to the task. Tune tree count, depth, minimum leaf size and feature subsampling, watching both predictive quality and model size. Validate with the correct temporal or group boundaries instead of relying solely on out-of-bag scores. Examine permutation importance and error slices while recognizing correlated predictors. The result should compare the forest with a simple baseline and explain its operational footprint, including the cost of storing and evaluating many trees.

Randomization does not eliminate leakage or guarantee independence among trees. Forests can be large and may extrapolate poorly outside observed ranges. Impurity-based importance can favor features with many possible splits, while correlated features complicate permutation interpretation. Out-of-bag evaluation is not a substitute for a deployment-relevant test when observations share entities or time dependence. A forest probability may require calibration. Readable component trees do not make the complete ensemble a concise decision rule.