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Learning with Subset Stacking (LESS)\n\nLESS 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).\n\n![LESS](./img/LESS1Level.png)\n\n## Installation\n\n`pip install less-learn`\n\nor\n\n``conda install -c conda-forge less-learn``\n\n(see also [conda-smithy repository](https://github.com/conda-forge/less-learn-feedstock))\n\n## Testing\n\nHere is how you can use LESS:\n\n```python\nfrom sklearn.datasets import make_regression, make_classification\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error, accuracy_score\nfrom less import LESSRegressor, LESSClassifier\n\n### CLASSIFICATION ###\n\nX, y = make_classification(n_samples=1000, n_features=20, n_classes=3, \\\n                           n_clusters_per_class=2, n_informative=10, random_state=42)\n\n# Train and test split\nX_train, X_test, y_train, y_test = \\\n    train_test_split(X, y, test_size=0.3, random_state=42)\n\n# LESS fit() \u0026 predict()\nLESS_model = LESSClassifier(random_state=42)\nLESS_model.fit(X_train, y_train)\ny_pred = LESS_model.predict(X_test)\nprint('Test accuracy of LESS: {0:.2f}'.format(accuracy_score(y_pred, y_test)))\n\n\n### REGRESSION ###\n\nX, y = make_regression(n_samples=1000, n_features=20, random_state=42)\n\n# Train and test split\nX_train, X_test, y_train, y_test = \\\n    train_test_split(X, y, test_size=0.3, random_state=42)\n\n# LESS fit() \u0026 predict()\nLESS_model = LESSRegressor(random_state=42)\nLESS_model.fit(X_train, y_train)\ny_pred = LESS_model.predict(X_test)\nprint('Test error of LESS: {0:.2f}'.format(mean_squared_error(y_pred, y_test)))\n\n```\n\n## Tutorials\n\nOur **two-part** [tutorial on Colab](https://colab.research.google.com/drive/183MRHH-i4XT3-HepHbIKVRPiwH7uMzrw?usp=sharing) aims at getting you familiar with LESS **regression**. If you want to try the tutorials on your own computer, then you also need to install the following additional packages: `pandas`, `matplotlib`, and `seaborn`.\n\n## Recommendation\n\nDefault implementation of LESS uses Euclidean distances with radial basis function. Therefore, it is a good idea to scale the input data before fitting. This can be done by setting the parameter `scaling` in `LESSRegressor` or `LESSClassifier` to `True` (this is the default value) or by preprocessing the data as follows:\n\n```python\nfrom sklearn.preprocessing import StandardScaler\n\nSC = StandardarScaler()\nX_train = SC.fit_transform(X_train)\nX_test = SC.transform(X_test)\n```\n\n## R Version (outdated)\n\nR implementation of an **older version** of LESS is available in [another repository](https://github.com/sibirbil/LESS-R).\n\n## Citation\nOur software can be cited as:\n````\n  @misc{LESS,\n    author = \"Ilker Birbil \u0026 Samet Copur\",\n    title = \"LESS: LEarning with Subset Stacking\",\n    year = 2025,\n    url = \"https://github.com/sibirbil/LESS/\"\n  }\n````\n\n## Changes in v.0.2.0\n\n* Classification is added (`LESSClassifier`)\n* Scaling is automatically done as default (`scaling = True`)\n* The default global estimator for regression is now `DecisionTreeRegressor` instead of `LinearRegression` (`global_estimator=DecisionTreeRegressor`)\n* Warnings can be turned on or off with a flag (`warnings = True`)\n\n## Changes in v.0.3.0\n\n* Typos are corrected\n* The hidden class for the binary classifier is now separate\n* Local subsets with a single class are handled (the case of `ConstantPredictor`)\n\n---\n\n#### Acknowledgments\n\nWe thank Oguz Albayrak for his help with structuring our initial Python scripts.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsibirbil%2Fless","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsibirbil%2Fless","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsibirbil%2Fless/lists"}