ridge_regression — Logtalk 3.100.1

# `ridge_regression`

Ridge regression regressor supporting continuous and mixed-feature datasets. The library implements the `regressor_protocol` defined in the `regression_protocols` library and learns a linear model by solving the weighted ridge normal equations directly via the shared regression encoding core in `regressor_common`, leaving the intercept unpenalized while penalizing encoded feature columns using scale-aware weights that match standardizing penalized columns before applying the L2 penalty.

## API documentation

Open the ../../apis/library_index.html#ridge_regression link in a web browser.

## Loading

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

    | ?- logtalk_load(ridge_regression(loader)).

## Testing

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

    | ?- logtalk_load(ridge_regression(tester)).

To run the performance benchmark suite, load the `tester_performance.lgt` file:

    | ?- logtalk_load(ridge_regression(tester_performance)).

## Features

- Continuous and Mixed Features: Supports continuous attributes and categorical attributes encoded using reference-level dummy coding.

- Feature Scaling and Penalty Scaling: Continuous attributes can be standardized using z-score scaling. Ridge regularization uses scale-aware weights equivalent to standardizing each penalized encoded feature column before applying the L2 penalty.

- Missing Values: Missing numeric and categorical values represented using anonymous variables are encoded using explicit missing-value indicator features.

- Unknown Values: Prediction requests containing categorical values that are not declared by the dataset raise a domain error.

- Zero-Variance Features: Encoded columns with zero variance are excluded from the direct solve and assigned zero coefficients in the learned regressor.

- Ridge Penalty: Applies L2 regularization to the learned weights using the shared `regularization/1` option.

- Diagnostics Metadata: Learned regressors record model name, target, training example count, solver, linear-system residual, active feature count, penalty scaling strategy, encoded feature count, and effective options, accessible using the shared regression diagnostics predicates.

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

- Reference Benchmarks: Includes a dedicated performance suite reporting training time, RMSE, and MAE for representative regression datasets.

## Regressor representation

The learned regressor is represented by default as:

- `ridge_regressor(Encoders, Bias, Weights, Diagnostics)`

The exported predicate clauses therefore use the shape:

- `Functor(Encoders, Bias, Weights, Diagnostics)`

## Diagnostics syntax

The `diagnostics/2` predicate returns a list of metadata terms with the form:

    [
        model(ridge_regression),
        target(Target),
        training_example_count(TrainingExampleCount),
        options(Options),
        solver(Solver),
        linear_system_residual(Residual),
        active_feature_count(ActiveFeatureCount),
        penalty_scaling(encoded_feature_standardization),
        encoded_feature_count(FeatureCount)
    ]

Where:

- `model(ridge_regression)` identifies the learning algorithm that produced the regressor.

- `target(Target)` stores the target attribute name declared by the training dataset.

- `training_example_count(TrainingExampleCount)` stores the number of examples used during training.

- `options(Options)` stores the effective learning options after merging the user options with the library defaults.

- `solver(Solver)` records the direct linear-system solver used during training. The current value is `pivoted_gaussian_elimination`.

- `linear_system_residual(Residual)` stores the maximum absolute residual of the solved ridge linear system.

- `active_feature_count(ActiveFeatureCount)` stores the number of encoded feature columns retained for the direct solve after dropping zero-variance columns.

- `penalty_scaling(encoded_feature_standardization)` records that the ridge penalty is scaled as if each penalized encoded feature column had been standardized before applying the L2 penalty.

- `encoded_feature_count(FeatureCount)` stores the number of numeric features induced by the encoder list, including missing-value indicator features.

Use the `regression_protocols` `diagnostic/2` and `regressor_options/2` helper predicates when you only need a single metadata term or the effective options.

## Options

The `learn/3` predicate accepts the following options:

- `regularization/1`: Ridge penalty coefficient applied to the weight vector during the direct solve. Higher values increase shrinkage and can reduce overfitting. The default is `0.01`.

- `feature_scaling/1`: Controls z-score standardization of continuous attributes before training and prediction. Accepted values are `true` and `false`. The default is `true`.