How to select model using Grid Search in Python

When building a machine learning model, it’s important to pick the right model that works best for the dataset. One way to do this is by using grid search to compare different models.

Grid search is a method that allows you to evaluate multiple models with different hyper-parameters, and select the one that has the best performance. This method tests all possible combinations of hyper-parameters for each model, and returns the best model based on a specific scoring metric.

In Python, the library scikit-learn provides an easy way to perform grid search 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 models you want to evaluate.

After that, you can use the GridSearchCV() function, which takes the list of models, the dataset, a dictionary of hyper-parameters and their possible values, and the scoring metric as inputs. The function returns the best model 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, grid search is a powerful tool for selecting the best model for a dataset. By using the GridSearchCV function in scikit-learn, it’s easy to compare different models 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 select model using Grid Search in Python.