regression_protocols — Logtalk 3.100.1

# `regression_protocols`

This library provides protocols used in the implementation of machine learning regression algorithms. Datasets are represented as objects implementing the `regression_dataset_protocol` protocol. Regressors are represented as objects importing the `regressor_common` category. This category provides shared helpers for regressor defaults, dataset validation, diagnostics metadata, export, and pretty-printing support.

Learned regressors expose diagnostics using the shared `diagnostics/2`, `diagnostic/2`, and `regressor_options/2` predicates. Concrete regressor implementations store effective training options in the diagnostics metadata under an `options(Options)` term.

This library also provides regression test datasets under the `test_datasets` directory.

## API documentation

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

## Loading

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

    | ?- logtalk_load(regression_protocols(loader)).

## Testing

To test this library predicates, shared categories, and datasets, load the `tester.lgt` file:

| ?- logtalk_load(regression_protocols(tester)).

## Test datasets

The `test_datasets` directory includes the following compact regression datasets and validation fixtures:

- `duplicate_attribute_declaration.lgt`: invalid dataset fixture containing a duplicated attribute declaration in the dataset schema.

- `duplicate_attribute_example.lgt`: invalid dataset fixture containing a duplicated declared attribute binding in one example.

- `grouped_categorical_signal.lgt`: regression dataset with one relevant continuous attribute and one irrelevant categorical attribute for testing shrinkage of encoded categorical coefficients.

- `intercept_only.lgt`: constant-target dataset for intercept-learning tests.

- `invalid_target.lgt`: invalid dataset with a non-numeric target for negative tests.

- `mixed_signal.lgt`: mixed continuous and categorical regression dataset.

- `plane.lgt`: two-feature regression dataset following the plane `z = 3x1 - 2x2 + 5`.

- `simple_line.lgt`: single-feature regression dataset following the line `y = 2x + 1`.

- `sparse_mixed_signal.lgt`: mixed regression dataset with omitted attribute-value pairs used to exercise missing-value handling during training and prediction.

- `sparse_signal.lgt`: two-feature regression dataset where only the signal attribute contributes to the target and the noise attribute is orthogonal to it.

- `step_signal.lgt`: piecewise-constant regression dataset for tree-based and neighborhood regressor testing.

- `undeclared_attribute_example.lgt`: invalid dataset fixture containing an undeclared attribute binding in one example.

- `wide_mixed_signal.lgt`: synthetic wide mixed regression dataset with many continuous and categorical predictors for non-trivial linear-model benchmarking.