How to tune hyper-parameters using GridSearchCV in Python

When building a machine learning model, it’s important to optimize the parameters of the model for the best performance. One way to do this is by tuning the hyper-parameters using GridSearchCV.

GridSearchCV is a method that allows you to search for the best combination of hyper-parameters, by training and evaluating a model using different 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 GridSearchCV using the function GridSearchCV().

The first step is to import the library and load the dataset into a pandas dataframe. Then, split the data into training and testing sets, and create an instance of the model you want to evaluate.

After that, you can use the GridSearchCV() 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 cv parameter that takes the number of splits you would like to make, or an iterable that you can use to define the splits.

Additionally, you can use the refit parameter, it will refit the estimator with best hyper-parameters using all available data.

In summary, GridSearchCV is a powerful tool for tuning the hyper-parameters of a machine learning model. By using the GridSearchCV 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.

In this Learn through Codes example, you will learn: How to tune hyper-parameters using GridSearchCV in Python.