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| Local Cascade Ensemble (LCE) is a high-performing, scalable and user-friendly machine learning method for the general tasks of Classification and Regression. | In particular, LCE:

Enhances the prediction performance of Random Forest and XGBoost by combining their strengths and adopting a complementary diversification approach
Supports parallel processing to ensure scalability
Handles missing data by design
Adopts scikit-learn API for the ease of use
Adheres to scikit-learn conventions to allow interaction with scikit-learn pipelines and model selection tools
Is released in open source and commercially usable - Apache 2.0 license

Getting Started

This section presents a quick start tutorial showing snippets for you to try out LCE.

Installation

You can install LCE from PyPI <https://pypi.org/project/lcensemble/>_ with pip::

pip install lcensemble

Or conda::

conda install -c conda-forge lcensemble

First Example on Iris Dataset

LCEClassifier accuracy on an Iris test set:

.. code-block:: python

from lce import LCEClassifier
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split

# Load data and generate a train/test split
data = load_iris()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, random_state=0)

# Train LCEClassifier with default parameters
clf = LCEClassifier(n_jobs=-1, random_state=0)
clf.fit(X_train, y_train)

# Make prediction and compute accuracy score
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: {:.1f}%".format(accuracy*100))

.. code-block::

Accuracy: 97.4%

Documentation

LCE documentation, including API documentation and general examples, can be found here <https://lce.readthedocs.io/en/latest/>_.

Contribute to LCE

Your valuable contribution will help make this package more powerful, and better for the community. There are multiple ways to participate, check out this page <https://lce.readthedocs.io/en/latest/contribute.html>_!

Reference Papers

LCE originated from a research at Inria, France <https://www.inria.fr/en>_. Here are the reference papers:

.. [1] Fauvel, K., E. Fromont, V. Masson, P. Faverdin and A. Termier. LCE: An Augmented Combination of Bagging and Boosting in Python. arXiv, 2023

.. [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

.. [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 & Data Mining, 2019

If you use LCE, we would appreciate citations.

Contact

If you have any question, you can contact me here: Kevin Fauvel <https://www.linkedin.com/in/kevin-fauvel-phd-cfa-caia-51b7777a/>_.

LocalCascadeEnsemble / LCE

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