mlens
A Python library for building high-performance, memory-efficient ensemble learning networks with a Scikit-learn compatible API.
What is mlens?
ML-Ensemble is a Python library for building high-performance ensemble learning models. It uses a computational graph framework to create memory-efficient, parallelized ensemble networks with a Scikit-learn compatible API, simplifying the construction of complex ensemble architectures.
Data scientists and machine learning engineers who need to build scalable, efficient ensemble models for predictive tasks, particularly those working with large datasets or requiring advanced model stacking techniques.
Developers choose ML-Ensemble for its unique combination of low-level computational graph flexibility and high-level Scikit-learn simplicity, enabling fast, memory-optimized ensemble construction without sacrificing design freedom or ease of use.
Overview
ML-Ensemble – high performance ensemble learning
Use Cases
Best For
- Building stacked generalization ensembles with multiple preprocessing pipelines
- Creating memory-efficient ensemble models for large-scale datasets
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