Hello and welcome! In today's lesson, we dive into the world of Decision Trees in text classification. Decision Trees are simple yet powerful supervised learning algorithms used for classification and regression problems. In this lesson, our focus will be on understanding the Decision Tree algorithm and implementing it for a text classification problem. Let's get started!

Decision Trees are a type of flowchart-like structure in which each internal node represents a feature, each branch represents a decision rule, and each leaf node represents an outcome or a class label. The topmost node in a Decision Tree is known as the root node, which best splits the dataset.

Splitting is a process of dividing a node into two or more sub-nodes, and a Decision Tree uses certain metrics during this training phase to find the best split. These include Entropy, Gini Index, and Information Gain.

The advantage of Decision Trees is that they require relatively little effort for data preparation yet can handle both categorical and numeric data. They are visually intuitive and easy to interpret.

Let's see how this interprets to our spam detection problem.

Before we dive into implementing Decision Trees, let's quickly load and preprocess our text dataset. This step will transform our dataset into a format that can be input into our machine learning models. This code block is being included for completeness: