Module ensemble 

SGBT ensemble orchestrator — the core boosting loop.

Implements Streaming Gradient Boosted Trees (Gunasekara et al., 2024): a sequence of boosting steps, each owning a streaming tree and drift detector, with automatic tree replacement when concept drift is detected.

Algorithm

For each incoming sample (x, y):

1. Compute the current ensemble prediction: F(x) = base + lr * Σ tree_s(x)
2. For each boosting step s = 1..N:
   • Compute gradient g = loss.gradient(y, current_pred)
   • Compute hessian h = loss.hessian(y, current_pred)
   • Feed (x, g, h) to tree s (which internally uses weighted squared loss)
   • Update current_pred += lr * tree_s.predict(x)
3. The ensemble adapts incrementally, with each tree targeting the residual of all preceding trees.

Modules

bagged

Bagged SGBT ensemble using Oza online bagging (Poisson weighting).

config

SGBT configuration with builder pattern and full validation.

multi_target

Multi-target regression via parallel SGBT ensembles.

multiclass

Multi-class classification via one-vs-rest SGBT committees.

parallel

Parallel SGBT training with delayed gradient updates.

quantile_regressor

Non-crossing multi-quantile regression via parallel SGBT ensembles.

replacement

TreeSlot: warning/danger/swap lifecycle for tree replacement.

step

Single boosting step: owns one tree + drift detector + optional alternate.

variants

SGBT computational variants (Gunasekara et al., 2024).

Structs

SGBT

Streaming Gradient Boosted Trees ensemble.

Type Aliases

DynSGBT

Type alias for an SGBT model using dynamic (boxed) loss dispatch.