Hello and welcome to the Decision Tree Models for Decision Making lesson. We will be using the Iris Dataset and the Sklearn library in Python to understand the intricate universe of Decision Trees.

This lesson will enable you to grasp the basic concepts of training, implementing, and making predictions with decision tree models. In conclusion, you should have an in-depth understanding of how to implement decision tree models using the Sklearn library in Python, how to train a decision tree model, and how to make predictions using the model.

We aim for a comprehensive grasp of decision tree models, from understanding the theory to implementing them practically on a real dataset. This enriched experience will undoubtedly boost your journey in the world of machine learning.

A Decision Tree model is a highly intuitive tool that uses a tree-like graph or model of decisions and their potential outcomes. It's essentially a structure similar to a flowchart, where each internal node denotes a test on an attribute, each branch represents the outcome of this test, and each leaf node (terminal node) holds a class label.

To help understand, think of decision tree models as tools for playing the game of "20 Questions". The game guesses what you're thinking by asking 20 'Yes' or 'No' questions. Each question progressively refines the possible answers, ultimately leading to the correct prediction.

In context, let's break down decision trees:

• Nodes: These represent the attribute or feature that the model uses to make the decision.
• Edges/Branches: These signify the outcome of a decision, forming a link to the next decision.
• Leaf Nodes: These are the final outcomes or the predictions of the decision tree.

Now that we have the basics of decision tree models let's explore how to train them.