Module: aprender::tree

Public module of the aprender-core crate.

Source

crates/aprender-core/src/tree.rs or directory.

Example

use aprender::tree::{DecisionTreeClassifier, RandomForestClassifier, GradientBoostingClassifier};
// See `cargo doc -p aprender-core --open` for full API reference.

Module summary

aprender::tree is the decision-tree family. It owns CART-style decision trees for both classification (DecisionTreeClassifier) and regression (DecisionTreeRegressor), plus the ensemble methods that wrap them: random forests (RandomForestClassifier, RandomForestRegressor) and gradient boosting (GradientBoostingClassifier). These are the workhorses for tabular data — fast to train, interpretable splits, no need to scale features.

Key types

TypeDescription
DecisionTreeClassifierCART classifier with Gini impurity, configurable max depth and min samples.
DecisionTreeRegressorCART regressor with MSE splits.
RandomForestClassifierBagged forest with bootstrap + feature subsampling.
RandomForestRegressorRandom-forest regressor.
GradientBoostingClassifierStage-wise additive boosting with shallow trees.
gini_impurity, gini_splitLow-level helpers re-exported for custom split algorithms.
Node, Leaf, TreeNodeInternal tree-node types exposed for inspection.

Usage patterns

Pattern 1: Decision tree with controlled depth

use aprender::metrics::classification::accuracy;
use aprender::prelude::*;

let x = Matrix::from_vec(6, 2, vec![
    1.0, 2.0, 2.0, 3.0, 3.0, 1.0,
    6.0, 5.0, 7.0, 8.0, 8.0, 6.0,
]).expect("6x2");
let y: Vec<usize> = vec![0, 0, 0, 1, 1, 1];

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

let preds = tree.predict(&x);
let acc = accuracy(&preds, &y);
assert!(acc >= 0.8, "training accuracy contract");

Pattern 2: Random-forest regressor

use aprender::tree::RandomForestRegressor;
use aprender::primitives::{Matrix, Vector};
use aprender::traits::Estimator;

let x = Matrix::from_vec(6, 1, vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).expect("6x1");
let y = Vector::from_slice(&[1.5, 3.1, 4.4, 6.0, 7.7, 9.5]);

let mut rf = RandomForestRegressor::new(50);
rf.fit(&x, &y).expect("rf fit");

let probe = Matrix::from_vec(1, 1, vec![3.5]).expect("1x1");
let pred = rf.predict(&probe);
println!("rf(3.5) ≈ {:.2}", pred[0]);

See also

  • ensemble — mixture-of-experts ensembles (differentiable routing)
  • classification — linear / instance-based classifiers
  • linear_model — linear regression baselines for tabular data
  • metrics — accuracy, r_squared to evaluate tree models

Full API

Run cargo doc -p aprender-core --open for the rendered rustdoc, or browse docs.rs/aprender for the published version.