neurodata/treeple
Scikit-learn compatible decision trees beyond those offered in scikit-learn
This project helps data scientists and machine learning engineers analyze data and make predictions using advanced decision tree models. It takes your raw datasets and builds powerful models for tasks like classification or regression, giving you insights and predictions that are often more accurate than standard methods. It's designed for practitioners who already work with tree-based models and want to explore more sophisticated options.
Use this if you are a data scientist or machine learning engineer using decision trees and need access to more advanced models like oblique, unsupervised, or quantile trees that offer improved performance for complex data or limited samples.
Not ideal if you are new to machine learning or only require basic, axis-aligned decision tree models, which are readily available in standard libraries.
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Last pushed
Mar 09, 2026
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