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:
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
This section presents a quick start tutorial showing snippets for you to try out LCE.
You can install LCE from PyPI with pip:
pip install lcensembleOr conda:
conda install -c conda-forge lcensembleLCEClassifier accuracy on an Iris test set:
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))Accuracy: 97.4%LCE documentation, including API documentation and general examples, can be found here.
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!
LCE originated from a research at Inria, France. Here are the reference papers:
If you use LCE, we would appreciate citations.
If you have any question, you can contact me here: Kevin Fauvel.