Grid Search Using Random Forest Algorithm

A practical example of the grid search method

This is another example of how we can use the grid search method to optimize the hyperparameters of the ML model.

In this example, we’ll use the random forest algorithm to determine which combination of hyperparameter values will produce the best results compared to the results obtained by using the default values for the hyperparameters.

What will we learn?

In this lesson, we’ll learn how to do the following things in the Jupyter Notebook:

• Create and train an ML model (random forest algorithm).

• Measure the performance of the ML model.

• perform the necessary steps to implement the grid search method.

• Identify the combination of hyperparameters that provide the best results.

Import important packages

First, we import the important Python packages that will do the following tasks:

• Create and train an ML model (random forest algorithm).

• Check the ML model performance.

• Implement the grid search method.

• Identify the combination of hyperparameters that provide the best results.