Welcome! Today, we are going to learn about the Decision Tree Classifier. It's one of the basic tools in machine learning that helps us make decisions like a flowchart. Imagine deciding whether to wear a coat. If it's cold, you wear it; if not, you don't. This is similar to how a Decision Tree works in predicting outcomes based on given data.

By the end of this lesson, you will know:

1. How to train a Decision Tree Classifier to make predictions.
2. The concept and learning process of a decision tree.
3. General parameters of a decision tree.

Let's start by looking at each of these steps one by one.

In machine learning, data is very important. We will use the wine dataset from Scikit-Learn. As a reminder, this dataset has measurements of different wines, and our goal is to predict the class of wine.

Here's a quick reminder on how to load and split this dataset:

A Decision Tree is a type of supervised learning model used for classification and regression tasks. It is a flowchart-like structure where:

• Root node represents one feature of the data.
• Internal nodes represent features (or attributes) of the data.
• Branches represent the decision rules.