Struct HistGradientBoostingClassifier 

#[non_exhaustive]
pub struct HistGradientBoostingClassifier { /* private fields */ }

Histogram-based Gradient Boosting for classification (binary + multiclass).

Uses the same O(256) histogram approach as the regressor, with log-loss for binary classification and softmax for multiclass. Leaf-wise tree growth with Newton-Raphson leaf correction.

Example

use scry_learn::dataset::Dataset;
use scry_learn::tree::HistGradientBoostingClassifier;

let features = vec![
    vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
    vec![0.1, 0.2, 0.3, 0.4, 0.5, 0.6],
];
let target = vec![0.0, 0.0, 0.0, 1.0, 1.0, 1.0];
let data = Dataset::new(features, target, vec!["x1".into(), "x2".into()], "class");

let mut model = HistGradientBoostingClassifier::new()
    .n_estimators(50)
    .learning_rate(0.1)
    .max_leaf_nodes(31);
model.fit(&data).unwrap();

let preds = model.predict(&[vec![1.5, 0.15], vec![5.5, 0.55]]).unwrap();
assert_eq!(preds[0], 0.0);
assert_eq!(preds[1], 1.0);

Implementations

impl HistGradientBoostingClassifier

pub fn new() -> Self

Create a new classifier with default parameters.

Example

use scry_learn::tree::HistGradientBoostingClassifier;

let model = HistGradientBoostingClassifier::new()
    .n_estimators(200)
    .learning_rate(0.05);

pub fn n_estimators(self, n: usize) -> Self

Set number of boosting rounds (default: 100).

pub fn learning_rate(self, lr: f64) -> Self

Set learning rate / shrinkage (default: 0.1).

pub fn max_leaf_nodes(self, n: usize) -> Self

Set maximum leaf nodes per tree (default: 31).

pub fn min_samples_leaf(self, n: usize) -> Self

Set minimum samples per leaf (default: 20).

pub fn max_depth(self, d: usize) -> Self

Set maximum tree depth (default: 8).

pub fn max_bins(self, bins: usize) -> Self

Set maximum bins (2..=256, default: 256).

pub fn l2_regularization(self, l2: f64) -> Self

Set L2 regularization (default: 0.0).

pub fn seed(self, s: u64) -> Self

Set random seed (default: 42).

pub fn fit(&mut self, data: &Dataset) -> Result<()>

Train the histogram-based gradient boosting classifier.

pub fn predict(&self, features: &[Vec<f64>]) -> Result<Vec<f64>>

Predict class labels for new samples.

pub fn predict_proba(&self, features: &[Vec<f64>]) -> Result<Vec<Vec<f64>>>

Predict class probabilities for new samples.

pub fn feature_importances(&self) -> Result<Vec<f64>>

Feature importances (total gain, normalized).

pub fn n_trees(&self) -> usize

Number of trees in the ensemble.

pub fn n_classes(&self) -> usize

Number of classes.

pub fn n_features(&self) -> usize

Number of features the model was trained on.

pub fn learning_rate_val(&self) -> f64

Learning rate value.

pub fn init_predictions_val(&self) -> &[f64]

Initial predictions per class.

pub fn class_tree_node_views(&self) -> Vec<Vec<Vec<HistNodeView>>>

Convert internal HistTree nodes to public HistNodeView arrays for ONNX export. Returns class_tree_views[class_idx][tree_idx] = Vec of HistNodeView.

Trait Implementations

impl CalibrableClassifier for HistGradientBoostingClassifier

fn fit(&mut self, data: &Dataset) -> Result<()>

Train on a dataset.

fn predict(&self, features: &[Vec<f64>]) -> Result<Vec<f64>>

Predict class labels.

fn predict_proba(&self, features: &[Vec<f64>]) -> Result<Vec<Vec<f64>>>

Predict class probabilities. Returns [n_samples][n_classes].

fn clone_box(&self) -> Box<dyn CalibrableClassifier>

Clone into a boxed trait object.

impl Clone for HistGradientBoostingClassifier

fn clone(&self) -> HistGradientBoostingClassifier

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Default for HistGradientBoostingClassifier

fn default() -> Self

Returns the “default value” for a type.

impl PipelineModel for HistGradientBoostingClassifier

fn fit(&mut self, data: &Dataset) -> Result<()>

Train the model on a dataset.

fn predict(&self, features: &[Vec<f64>]) -> Result<Vec<f64>>

Predict on row-major feature matrix.

impl Tunable for HistGradientBoostingClassifier

fn set_param(&mut self, name: &str, _value: ParamValue) -> Result<()>

Apply a named hyperparameter.

fn clone_box(&self) -> Box<dyn Tunable>

Clone this model into a boxed trait object.

fn fit(&mut self, data: &Dataset) -> Result<()>

Train on a dataset.

fn predict(&self, features: &[Vec<f64>]) -> Result<Vec<f64>>

Predict on row-major features.