# `gradient_boosting_classifier`

Gradient boosting classifier for tabular datasets using multinomial additive models fitted by regression trees to softmax residuals. At each boosting stage the implementation fits one regression tree per class and updates additive class scores using the configured learning rate.

The library implements the `classifier_protocol` defined in the `classification_protocols` library. It provides predicates for learning a classifier from a dataset, using it to make predictions, estimating class probabilities, and exporting it as a list of predicate clauses or to a file.

Datasets are represented as objects implementing the `dataset_protocol` protocol from the `classification_protocols` library. Continuous, categorical, and mixed-feature datasets are supported through the reused `regression_tree` backend.

## API documentation

Open the ../../docs/library_index.html#gradient_boosting_classifier link in a web browser.

## Loading

To load this library, load the `loader.lgt` file:

    | ?- logtalk_load(gradient_boosting_classifier(loader)).

## Testing

To test this library predicates, load the `tester.lgt` file:

    | ?- logtalk_load(gradient_boosting_classifier(tester)).

## Features

- Multiclass Boosting: Fits one additive score function per class and predicts using the highest boosted score.

- Regression Tree Backend: Reuses the `regression_tree` library to fit residual models at each boosting stage.

- Probability Estimation: Provides class probabilities using a softmax over the final additive scores.

- Configurable Tree Complexity: Exposes boosting-stage tree depth, minimum leaf size, and minimum variance reduction options.

- Tabular Datasets: Supports continuous, categorical, and mixed-feature datasets.

- Classifier Export: Learned classifiers can be exported as predicate clauses or written to a file.

## Options

The `learn/3` predicate supports these options:

- `number_of_estimators/1` - number of boosting stages to fit (default: `25`)

- `learning_rate/1` - shrinkage factor applied to each stage prediction (default: `0.1`)

- `maximum_depth/1` - maximum depth of each regression tree (default: `3`)

- `minimum_samples_leaf/1` - minimum number of examples in a leaf (default: `1`)

- `minimum_variance_reduction/1` - minimum variance reduction required for a split (default: `0.0`)

- `feature_scaling/1` - whether to scale continuous features in the regression-tree backend (default: `false`)

## Usage

### Learning a classifier

    | ?- gradient_boosting_classifier::learn(weather, Classifier).

    | ?- gradient_boosting_classifier::learn(iris_small, Classifier, [number_of_estimators(50), learning_rate(0.05)]).

### Making predictions

    | ?- gradient_boosting_classifier::learn(weather, Classifier),
         gradient_boosting_classifier::predict(Classifier, [outlook-overcast, temperature-mild, humidity-high, windy-false], Class).

    | ?- gradient_boosting_classifier::learn(mixed, Classifier),
         gradient_boosting_classifier::predict_probabilities(Classifier, [age-40, income-60000, student-yes, credit_rating-fair], Probabilities).

### Exporting the classifier

    | ?- gradient_boosting_classifier::learn(weather, Classifier),
         gradient_boosting_classifier::export_to_clauses(weather, Classifier, classify, Clauses).

    | ?- gradient_boosting_classifier::learn(weather, Classifier),
         gradient_boosting_classifier::export_to_file(weather, Classifier, classify, 'classifier.pl').

## Classifier representation

The learned classifier is represented as a compound term with the form:

    gradient_boosting_classifier(Classes, InitialScores, StageTrees, Options)

Where:

- `Classes`: list of class labels

- `InitialScores`: list of initial log-prior scores, one per class

- `StageTrees`: list of `stage_trees(ClassTrees)` terms, where each `ClassTrees` value contains `class_tree(Class, LearningRate, Tree)` terms

- `Options`: merged training options used to learn the classifier

When exported using `export_to_clauses/4` or `export_to_file/4`, this classifier term is serialized directly as the single argument of the generated predicate clause so that the exported model can be loaded and reused as-is.

## References

1. Friedman, J.H. (2001). “Greedy Function Approximation: A Gradient Boosting Machine”.

2. Hastie, T., Tibshirani, R. and Friedman, J. (2009). “The Elements of Statistical Learning”. Chapter 10.

3. Bishop, C.M. (2006). “Pattern Recognition and Machine Learning”. Section 14.4.