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Pack logtalk -- logtalk-3.100.1/docs/handbook/_sources/libraries/regression_protocols.rst.txt

.. _library_regression_protocols:

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 <../../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.