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https://github.com/localcascadeensemble/lce

Random Forest or XGBoost? It is Time to Explore LCE
https://github.com/localcascadeensemble/lce

classification data-science machine-learning python regression scikit-learn-api

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Random Forest or XGBoost? It is Time to Explore LCE

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README 

        

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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 `_ 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 `_.



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 `_!



Reference Papers

================



LCE originated from a research at `Inria, France `_. 

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 `_.