Hyperparameter Tuning with scikit-learn

Modern machine learning (ML) models often possess large numbers of hyperparameters, and combing through the hyperparameter search space to find their optimal values is no easy task. Consequently, several algorithms have been devised to do this.In this course, compare different hyperparameter tuning methods and explore how to implement them. Next, learn how to perform hyperparameter tuning on the decision tree regressor using GridSearchCV and RandomizedSearchCV and analyze the results of HalvingGridSearchCV and HalvingRandomSearchCV. Finally, examine how to switch from regression to classification and use them, and extend them via the use of multiple scoring parameters.After completing this video, you will be able to perform hyperparameter Tuning with scikit-learn.