.. _getting_started:

Getting Started
===============

LESS is a supervised learning algorithm that is based on training many local estimators on subsets of a given dataset, and then passing their predictions to a global estimator. You can find the details about LESS in our `manuscript <https://arxiv.org/abs/2112.06251>`_.

.. image:: _static/LESS1Level.png
   :alt: LESS

This guide provides a basic introduction to using the LESS library.

Installation
------------

Install ``LESS`` from PyPI:

.. code-block:: bash

    pip install less-learn

Example Usage
-------------

Below is a simplified example of how to use ``LESSBRegressor`` for a regression task.

.. code-block:: python

    from sklearn.datasets import make_regression
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import mean_squared_error
    from less import LESSBRegressor

    # Generate a synthetic regression dataset
    X, y = make_regression(n_samples=1000, n_features=20, random_state=42)

    # Split into training and testing sets
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.3, random_state=42
    )

    # Initialize and train the LESS model
    less_model = LESSBRegressor(random_state=42)
    less_model.fit(X_train, y_train)

    # Make predictions and evaluate performance
    y_pred = less_model.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    print(f"Test MSE of LESS: {mse:.2f}")

.. note::

    LESS employs Euclidean distances combined with a radial basis function (RBF) kernel by default.
    It is therefore *highly recommended* to scale or normalize input features prior to model training to ensure optimal performance.

Citation
--------

If you use LESS in your research, please cite our manuscript:

.. code-block:: bibtex

    @misc{LESS,
      author = "Ilker Birbil",
      title = "LESS: Learning with Subset Stacking",
      year = 2025,
      url = "https://github.com/sibirbil/LESS/"
    }