DFLsklearn, Hyperparameters optimization in Scikit Learn 0.1

Vittorio Latorre, Federico Benvenuto — Thu, 23 Nov 2017 13:14:36 -0000

DFLsklearn is a method that performs cross validation over the hyperparameters of the Scikit-learn methods based on an efficient derivative free mixed-integer line search algorithm called Derivative Free Line-search (DFL). DFL is an algorithm with deterministic convergence properties toward local stationary points of the objective function. Furthermore, the DFL algorithm is implemented in a highly optimized Fortran code.

This software is focused on performing the hyperparameters optimization for each single estimator of Scikit-learn, enabling expert users to exploit as much as possible the features of the machine learning method they are using.

The source code of DFLsklearn is available here on jmlr.org and GitHub (url{https://github.com/midagroup/DFLsklearn}) under the New BSD License. The code follows PEP8 standards. A setup.py file is provided in order to easily compile the Fortran code and install the module in the PythonPath. DFLsklearn just depends on Scikit-Learn package for easy portability and compatibility on different platforms.

mloss.org DFLsklearn, Hyperparameters optimization in Scikit Learn

DFLsklearn, Hyperparameters optimization in Scikit Learn 0.1