HistGradientBoostingRegressor
Histogram-based gradient boosting for regression — the fast, large-data GB path.
Algorithm
Features are quantile-binned, then squared-error boosting proceeds on the binned representation with histogram split finding.
Constructor
Skigen::HistGradientBoostingRegressor<Scalar> model(Scalar learning_rate = 0.1, int max_iter = 100, ...);
Parameters
| Parameter | Default | Description |
|---|---|---|
learning_rate | 0.1 | Shrinkage per iteration. |
max_iter | 100 | Number of iterations. |
max_bins | 255 | Bin resolution. |
max_leaf_nodes | 31 | Leaves per tree. |
random_state | nullopt | Seed. |
Methods
| Method | Description |
|---|---|
fit(X, y) | Bin features, then boost. |
predict(X) | Boosted prediction. |
score(X, y) | R². |
Fitted Attributes
| Accessor | Description |
|---|---|
bin_edges() | Per-feature quantile bin edges. |
train_score() | Per-iteration training MSE. |
Example
Skigen::HistGradientBoostingRegressor<double> gb;
gb.fit(X, y);
auto preds = gb.predict(X_test);
Verified against scikit-learn
This estimator is checked by the parity suite. See the generator tests/parity/generate_ensemble_reference.py and the reference fixtures in tests/parity/data/hist_gradient_boosting_regressor/, exercised by tests/parity/parity_ensemble.cpp.
API Reference
For full signatures see the HistGradientBoostingRegressor API Reference.