Module ensemble_learner 

EnsembleLearner — production-quality ensemble learning system.

Implements Bagging, AdaBoost, Gradient Boosting, Random Forest, and Stacking strategies over decision stumps and perceptron base models.

Design

• Bagging: bootstrap samples, parallel independent base models, majority/average vote.
• AdaBoost: sequential stump fitting, exponential weight update (alpha), sample re-weighting after each round.
• Gradient Boosting: pseudo-residual fitting, shrinkage via learning_rate.
• Random Forest: bagging + feature sub-sampling per stump (sqrt(n_features)).
• Stacking: train diverse base models and a linear meta-learner on their outputs.
• All operations use xorshift64 for bootstrap/sub-sampling — no rand crate.
• Training history is kept in a VecDeque<ElTrainingRecord> capped at 100 entries.
• No unwrap() anywhere — all fallible operations use ? / ok_or.

Re-exports

pub use type_aliases::*;

pub use types::*;

Modules

ellearnerconfig_traits

ElLearnerConfig - Trait Implementations

elmethod_traits

ElMethod - Trait Implementations

functions

Auto-generated module

type_aliases

Auto-generated module

types

Auto-generated module