Local Cascade Ensemble package
Project description
LCE: Local Cascade Ensemble
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
Installation
You can install LCE from PyPI with the following command:
pip install lce
First Example on Iris Dataset
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
LCE documentation can be found here.
Reference
The full information about LCE can be found in the associated journal paper.
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} }
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