softmin/ReHLine-python
Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence
This tool helps data scientists and machine learning engineers quickly build and optimize machine learning models for classification, regression, and constrained optimization problems. You input your dataset and choose a model type (like Support Vector Machines or Huber Regression), and it efficiently computes the optimal model parameters. It's designed for practitioners who need to train high-performing models on large datasets.
Use this if you need to train machine learning models for classification or regression, especially when dealing with large datasets or complex constraints, and require exceptional speed and efficiency.
Not ideal if you need to work with non-linear relationships or highly complex loss functions that are not piecewise linear-quadratic.
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18
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6
Language
Python
License
MIT
Category
Last pushed
Mar 10, 2026
Commits (30d)
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