{"id":15442801,"url":"https://github.com/localcascadeensemble/lce","last_synced_at":"2025-04-13T21:13:02.816Z","repository":{"id":36966847,"uuid":"481133244","full_name":"LocalCascadeEnsemble/LCE","owner":"LocalCascadeEnsemble","description":"Random Forest or XGBoost? 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raw:: html\n\n\t\u003cp align=\"center\"\u003e\n\t\t\u003cimg src=\"./logo/logo_lce.svg\" width=\"35%\"\u003e\t\n\t\u003c/p\u003e\n\t\n\t\u003cdiv align=\"center\"\u003e\n\t\t\u003ca href=\"https://circleci.com/gh/LocalCascadeEnsemble/LCE/tree/main\"\u003e\n\t\t\t\u003cimg src=\"https://circleci.com/gh/LocalCascadeEnsemble/LCE/tree/main.svg?style=shield\"\u003e\n\t\t\u003c/a\u003e\n\t\t\u003ca href=\"https://codecov.io/gh/LocalCascadeEnsemble/LCE\"\u003e\n\t\t\t\u003cimg src=\"https://codecov.io/gh/LocalCascadeEnsemble/LCE/branch/main/graph/badge.svg?token=VTA64P4GTF\"\u003e\n\t\t\u003c/a\u003e\n\t\t\u003ca href=\"https://lce.readthedocs.io/en/latest/?badge=latest\"\u003e\n\t\t\t\u003cimg src=\"https://readthedocs.org/projects/lce/badge/?version=latest\"\u003e\n\t\t\u003c/a\u003e\n\t\t\u003ca href=\"https://pypi.python.org/pypi/lcensemble/\"\u003e\t\t\n\t\t\t\u003cimg src=\"https://badge.fury.io/py/lcensemble.svg\"\u003e\n\t\t\u003c/a\u003e\t\t\n\t\t\u003ca href=\"https://pypi.python.org/pypi/lcensemble/\"\u003e\t\t\n\t\t\t\u003cimg src=\"https://img.shields.io/pypi/pyversions/lcensemble.svg\"\u003e\n\t\t\u003c/a\u003e\n\t\t\u003ca href=\"https://github.com/psf/black\"\u003e\t\n\t\t\t\u003cimg src=\"https://img.shields.io/badge/code%20style-black-000000.svg\"\u003e\n\t\t\u003c/a\u003e\n\t\t\u003ca href=\"https://pypi.python.org/pypi/lcensemble/\"\u003e\t\t\n\t\t\t\u003cimg src=\"https://img.shields.io/github/license/LocalCascadeEnsemble/LCE.svg\"\u003e\n\t\t\u003c/a\u003e\n\t\u003c/div\u003e\n   \n| **Local Cascade Ensemble (LCE)** is a *high-performing*, *scalable* and *user-friendly* machine learning method for the general tasks of **Classification** and **Regression**.\n| In particular, LCE:\n \n- Enhances the prediction performance of Random Forest and XGBoost by combining their strengths and adopting a complementary diversification approach\n- Supports parallel processing to ensure scalability\n- Handles missing data by design\n- Adopts scikit-learn API for the ease of use\n- Adheres to scikit-learn conventions to allow interaction with scikit-learn pipelines and model selection tools\n- Is released in open source and commercially usable - Apache 2.0 license\n\n\nGetting Started\n===============\n\nThis section presents a quick start tutorial showing snippets for you to try out LCE.\n\nInstallation\n------------\n\nYou can install LCE from `PyPI \u003chttps://pypi.org/project/lcensemble/\u003e`_ with ``pip``::\n\n\tpip install lcensemble\n\t\nOr ``conda``::\n\n\tconda install -c conda-forge lcensemble\n\t\n\t\nFirst Example on Iris Dataset\n-----------------------------\n\nLCEClassifier accuracy on an Iris test set:\n\n.. code-block:: python\n\n\tfrom lce import LCEClassifier\n\tfrom sklearn.datasets import load_iris\n\tfrom sklearn.metrics import accuracy_score\n\tfrom sklearn.model_selection import train_test_split\n\n\n\t# Load data and generate a train/test split\n\tdata = load_iris()\n\tX_train, X_test, y_train, y_test = train_test_split(data.data, data.target, random_state=0)\n\n\t# Train LCEClassifier with default parameters\n\tclf = LCEClassifier(n_jobs=-1, random_state=0)\n\tclf.fit(X_train, y_train)\n\n\t# Make prediction and compute accuracy score\n\ty_pred = clf.predict(X_test)\n\taccuracy = accuracy_score(y_test, y_pred)\n\tprint(\"Accuracy: {:.1f}%\".format(accuracy*100))\n\t\n.. code-block::\n\t\n\tAccuracy: 97.4%\n\n\nDocumentation\n=============\n\nLCE documentation, including API documentation and general examples, can be found `here \u003chttps://lce.readthedocs.io/en/latest/\u003e`_.\n\n\nContribute to LCE\n=================\n\nYour valuable contribution will help make this package more powerful, and better for the community.\nThere are multiple ways to participate, check out this `page \u003chttps://lce.readthedocs.io/en/latest/contribute.html\u003e`_!\n\n\nReference Papers\n================\n\nLCE originated from a research at `Inria, France \u003chttps://www.inria.fr/en\u003e`_. \nHere are the reference papers:\n\n.. [1] Fauvel, K., E. Fromont, V. Masson, P. Faverdin and A. Termier. LCE: An Augmented Combination of Bagging and Boosting in Python. arXiv, 2023\n\n.. [2] Fauvel, K., E. Fromont, V. Masson, P. Faverdin and A. Termier. XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification. Data Mining and Knowledge Discovery, 36(3):917–957, 2022\n\n.. [3] Fauvel, K., V. Masson, E. Fromont, P. Faverdin and A. Termier. Towards Sustainable Dairy Management - A Machine Learning Enhanced Method for Estrus Detection. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery \u0026 Data Mining, 2019\n\nIf you use LCE, we would appreciate citations.\n\n\nContact\n=======\n\nIf you have any question, you can contact me here: `Kevin Fauvel \u003chttps://www.linkedin.com/in/kevin-fauvel-phd-cfa-caia-51b7777a/\u003e`_.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flocalcascadeensemble%2Flce","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flocalcascadeensemble%2Flce","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flocalcascadeensemble%2Flce/lists"}