lcensemble 0.1.7

Local Cascade Ensemble package

Local Cascade Ensemble (LCE) proposes to further enhance the prediction performance of the state-of-the-art Random Forest and XGBoost by combining their strengths and adopting a complementary implicit diversification. LCE is a hybrid ensemble method that combines an explicit boosting-bagging approach to handle the bias-variance trade-off faced by machine learning models and an implicit divide-and-conquer approach to individualize classifier errors on different parts of the training data. LCE has been evaluated on a public benchmark and published in the journal Data Mining and Knowledge Discovery.

LCE package is compatible with scikit-learn; it passes the check_estimator. Therefore, it can interact with scikit-learn pipelines and model selection tools.

Installation

---

You can install LCE from PyPI with the following command:

pip install lcensemble

Documentation

---

LCE documentation can be found here.

Reference

---

The full information about LCE can be found in the associated journal paper.

[1]

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, 2022. https://hal.inria.fr/hal-03599214/document