Introduction

Model search result and reporting

The class GridSearchCV of scikit-learn provides an easy way to get the cross-validation scores of an estimator for a grid of hyper-parameters. While this is a usefull feature, an often scenario in machine learning research is to design experiments where multiple estimators are compared for various hyper-parameter grids. This functionality is supported from research-learn by providing the ModelSearchCV class and the report_model_search_results() function.

Design and analysis of experiments

Setting up and analyzing the results of machine learning experiments is a time consuming procedure that requires multiple steps like data gathering and preparation, selection of estimators and their hyperparameters, analysis of the results and finally application of multiple statistical tests. The application of these steps is simplified by research-learn through the base BaseExperiment as well as appropriate functions to analyze the experimental results.