Welcome! Today, we're going to learn about an exciting and powerful tool in machine learning called Grid Search. Imagine trying to find the perfect pair of shoes that fit just right. Grid Search does something similar but for tuning machine learning models. By the end of this lesson, you'll understand how to use Grid Search to find the best settings (parameters) for your models.

Imagine baking the perfect cake. You need to find the right proportions of sugar, flour, and baking soda. Grid Search does the same for machine learning models by trying different combinations of parameters to find the best one. Parameters are settings you can adjust to improve your model's performance. The right parameters can make your model more accurate.

The parameters we set when initializing the model are called hyperparameters. Finding the perfect combination of them is called hypertuning.

We have already done some hypertuning before in this course path using for loops. But writing a for loop each time can be laborious, especially if you must check multiple models with multiple hyperparameters each. So, it is time for us to learn about a special tool that automates this process!

Let's implement Grid Search using Scikit-Learn.

First, load the libraries and the Wine dataset: