Module tree 

Decision tree algorithms and ensemble methods.

This module implements:

• CART (Classification and Regression Trees) using Gini impurity
• Random Forest ensemble classifier
• Gradient Boosting Machine (GBM) for sequential ensemble learning

Example

// Full API example (not yet implemented)
use aprender::prelude::*;
use aprender::tree::DecisionTreeClassifier;

// Training data (simple 2D binary classification)
let x = Matrix::from_vec(4, 2, vec![
    0.0, 0.0,  // class 0
    0.0, 1.0,  // class 1
    1.0, 0.0,  // class 1
    1.0, 1.0,  // class 0
]).expect("Matrix creation should succeed in tests");
let y = vec![0, 1, 1, 0];

// Train decision tree
let mut tree = DecisionTreeClassifier::new()
    .with_max_depth(3);
tree.fit(&x, &y).expect("fit should succeed");

// Make predictions
let predictions = tree.predict(&x);

Structs

DecisionTreeClassifier

Decision tree classifier using the CART algorithm.

DecisionTreeRegressor

Decision tree regressor using the CART algorithm.

GradientBoostingClassifier

Gradient Boosting Classifier.

Leaf

Leaf node in a decision tree.

Node

Internal node in a decision tree.

RandomForestClassifier

Random Forest classifier - an ensemble of decision trees.

RandomForestRegressor

Random Forest Regressor.

RegressionLeaf

Leaf node in a regression tree.

RegressionNode

Internal node in a regression tree.

Enums

RegressionTreeNode

A node in a regression tree (either internal node or leaf).

TreeNode

A node in a decision tree (either internal node or leaf).