Hyperparameter optimization with Python
Introduction In the previous articles we introduced several linear techniques, where as you have probably noticed, we provided the algorithms with several parameters. The dependence of machine learning algorithm upon learning parameters is a common case though and one has to check the performance of various parameters to achieve the best results. The task of course is no trifle and is called hyperparameter optimization or model selection. It is the problem of choosing a set of hyperparameters for a learning algorithm, usually with the goal of optimizing a measure of the algorithm's performance on an independent data set. Implementation Grid Search The traditional way of performing hyperparameter optimization is a grid search, or a parameter sweep, which is simply an exhaustive searching through a manually specified subset of the hyperparameter space of a learning algorithm. Scikit-learn provides us with a class GridSearchCV implementing the technique. Let's try to