How to tune hyper-parameters using RandomSearchCV in Python
RandomSearchCV is a method that allows you to search for the best combination of hyper-parameters, by training and evaluating a model using random combinations of hyper-parameters. It will pick the best combination of hyper-parameters based on the performance of the model.
In Python, the library scikit-learn provides an easy way to perform RandomSearchCV using the function
After that, you can use the
RandomSearchCV() function, which takes the model, the dataset, a dictionary of hyper-parameters and their possible values, and the scoring metric as inputs. The function returns the best combination of hyper-parameters based on the performance of the model.
You can use the
n_iter parameter that controls the number of combinations of hyper-parameters that will be tried.
Additionally, you can use the
refit parameter, it will refit the estimator with best hyper-parameters using all available data.
In summary, RandomSearchCV is a powerful tool for tuning the hyper-parameters of a machine learning model. By using the RandomSearchCV function in scikit-learn, it’s easy to tune the hyper-parameters in Python, making it a valuable tool for data scientists and machine learning practitioners. Keep in mind that, random search might not be as efficient as grid search in terms of time but it has been shown to be more efficient in finding better hyperparameters, especially when the search space is large.
Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.