DFLsklearn, Hyperparameters optimization in Scikit Learnhttp://mloss.orgUpdates and additions to DFLsklearn, Hyperparameters optimization in Scikit LearnenThu, 23 Nov 2017 13:14:36 -0000DFLsklearn, Hyperparameters optimization in Scikit Learn 0.1<html><p>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. </p> <p>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. </p> <p>The source code of DFLsklearn is available here on and GitHub (url{}) under the New BSD License. The code follows PEP8 standards. A 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. </p></html>Vittorio Latorre, Federico BenvenutoThu, 23 Nov 2017 13:14:36 -0000 learninghyperparameter selection