yunxiangz / lce Goto Github PK

Local Cascade Ensemble (LCE) is a machine learning method that further enhances the prediction performance of the state-of-the-art Random Forest and XGBoost. LCE combines their strengths and adopts a complementary diversification approach to obtain a better generalizing predictor. Specifically, 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.

You can install LCE from PyPI with pip:

pip install lcensemble

Or conda:

conda install -c conda-forge lcensemble

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(n_jobs=-1, 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))

LCE documentation can be found here.

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},
  volume = {36},
  number = {3},
  pages = {917-957}
}