How to find optimal parameters using GridSearchCV in Regression in Python

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How to find optimal parameters using GridSearchCV in Regression in Python

GridSearchCV is a method to find the best set of parameters for a machine learning model. It works by defining a range of parameters that you want to test and then evaluating the performance of the model for each combination of parameters. The goal is to find the combination of parameters that results in the best performance.

To use GridSearchCV, you first need to define the model you want to use and the range of parameters you want to test. Next, you use the GridSearchCV function to search for the best combination of parameters. This function takes the model, the parameter range, and some additional settings such as the number of cross-validation folds to use.

Once the grid search is finished, you can access the best parameters found by GridSearchCV by calling the “best_params_” attribute of the GridSearchCV object.

It is important to note that GridSearchCV is a powerful technique but it can be computationally expensive, especially when the number of parameters or the range of values for each parameter is large. In those cases, it may be more efficient to use more specialized techniques such as RandomizedSearchCV.


In this Machine Learning Recipe, you will learn: How to find optimal parameters using GridSearchCV in Regression in Python.

How to find optimal parameters using GridSearchCV in Regression in Python

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