lcensemble 0.1.0

Local Cascade Ensemble package

LCE: Local Cascade Ensemble

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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 way.

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.

Getting Started

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Installation

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You can install LCE from PyPI with the following command:

pip install lce

First Example on Iris Dataset

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LCEClassifier prediction on an Iris test set:

from lce import LCEClassifier

from sklearn.datasets import load_iris

from sklearn.metrics import classification_report

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(random_state=0)

clf.fit(X_train, y_train)



# Make prediction and generate classification report

y_pred = clf.predict(X_test)

print(classification_report(y_test, y_pred))

Documentation

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LCE documentation can be found here.

Reference

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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

If you use the package, please cite us with the following BibTex:

@article{Fauvel22-LCE,

  author = {Fauvel, K. and E. Fromont and V. Masson and P. Faverdin and A. Termier},

  title = {{XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification}},

  journal = {Data Mining and Knowledge Discovery},

  year = {2022},

  doi = {10.1007/s10618-022-00823-6}

}