ottenbreit-data-science/aplr
APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.
This tool helps data scientists and machine learning engineers build predictive models for either regression (predicting continuous values) or classification (predicting categories). You input your raw data, and it outputs a model that not only makes accurate predictions but also explains *how* it arrived at those predictions, which is crucial for understanding underlying patterns or business logic. This is for professionals who need both high accuracy and clear explanations from their models.
Use this if you need to build predictive models that are as accurate as complex methods but must also be easily understood and interpreted by stakeholders.
Not ideal if your primary concern is absolute maximum predictive accuracy at the expense of any interpretability, or if you prefer black-box models.
Stars
23
Forks
5
Language
C++
License
MIT
Last pushed
Feb 24, 2026
Commits (30d)
0
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