ix-random-forest

name	ix-random-forest
description	Random forest and gradient boosted trees — ensemble classifiers for tabular data
disable-model-invocation	true

Ensemble Methods

Ensemble classifiers using decision tree base learners.

When to Use

When the user needs classification with probability estimates, wants a robust baseline classifier, has tabular data, or needs gradient boosting for high accuracy.

Algorithms

Random Forest (Bagging)

• Each tree trained on bootstrap sample with random feature subsets
• Predictions via majority vote — reduces variance
• More trees → better performance (diminishing returns)
• Best for: stable baseline, feature importance, parallel training

Gradient Boosted Trees (Boosting)

• Sequentially fits trees to pseudo-residuals of log-loss
• Learning rate controls step size — lower = smoother but more rounds needed
• Best for: maximum accuracy on tabular data, tunable bias-variance tradeoff
• Supports binary and multiclass classification

Choosing Between Them

Criterion	Random Forest	Gradient Boosting
Tuning effort	Low (just n_trees)	Medium (LR + n_estimators)
Overfitting risk	Low	Higher (use low LR)
Accuracy ceiling	Good	Often better
Training speed	Fast (parallelizable)	Sequential rounds

Programmatic Usage

use ix_ensemble::random_forest::RandomForest;
use ix_ensemble::gradient_boosting::GradientBoostedClassifier;
use ix_ensemble::traits::EnsembleClassifier;

// Random Forest
let mut rf = RandomForest::new(100, 10).with_seed(42);
rf.fit(&x_train, &y_train);
let preds = rf.predict(&x_test);

// Gradient Boosting
let mut gbc = GradientBoostedClassifier::new(50, 0.1);
gbc.fit(&x_train, &y_train);
let preds = gbc.predict(&x_test);
let probas = gbc.predict_proba(&x_test);

MCP Tools

• ix_random_forest — Parameters: x_train, y_train, x_test, n_trees, max_depth
• ix_gradient_boosting — Parameters: x_train, y_train, x_test, n_estimators, learning_rate, max_depth