Genetic Algorithm Using Random Forest

This example shows how we can use the genetic algorithm 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 Jupyter Notebook:

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

• Measure the performance of the ML model.

• Perform the steps required to implement the genetic algorithm.

• 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’s performance.

• Implement the genetic algorithm.

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