softmin/ReHLine-r
Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence
This tool helps data scientists and analysts quickly find the best linear model for large datasets when dealing with complex, non-standard loss functions and specific constraints. You provide your data and the desired piecewise linear or quadratic loss function, and it outputs the optimal model coefficients. It's designed for practitioners who need to build robust predictive models efficiently.
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Use this if you need to build linear predictive models on large datasets, incorporating custom loss functions or specific equality/inequality constraints, and require fast, guaranteed convergence.
Not ideal if your problem involves non-linear relationships that cannot be approximated by piecewise linear-quadratic functions, or if you prefer tree-based or deep learning models.
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Language
C++
License
MIT
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Last pushed
Feb 10, 2024
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softmin/ReHLine-python
Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence