ML-Ensemble

High performance ensemble learning in Python

Learn more

from mlens.ensemble import SuperLearner 
ensemble = SuperLearner()
ensemble.add(estimators)
ensemble.add_meta(meta_estimator)
ensemble.fit(X, y).predict(X)
                  

A computational graph approach

Modular, flexible, memory neutral; embarrassingly parallel


Embarrasingly parallel

Optimized for maximum concurrency at minimum memory consumption

Modular

Build deep, complex ensembles in just a few lines of code

Flexible

Leverage low-level API for full control and dynamic computations

Dedicated diagnostics

Purpose-built model-selection suite for efficient ensemble learning

Installation



Open sourced under MIT
Free to use for commercial purposes

pip install mlens





Ensemble tutorials

Ensemble estimators
Model selection
Visualization
Advanced features


Get started


Graph Mechanics

Learner & transformer nodes
Layer groups
Parallel processing
Sequential stacking


Programmer's guide

Join the project at Github