Predicting Scenarios
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What’s Included
Predicting Using Classification
Predicting Using Classification
Classification problems occur when we want to predict a variable that can only have a small number of clearly defined values. In this lesson, we'll introduce the fundamental principles of classification.
Introducing Decision Trees
Introducing Decision Trees
Decision trees are a simple and intuitive classification algorithm. In this lesson, we learn how decision trees make predictions and introduce some of the terminology surrounding them.
Understanding Decision Tree Data
Understanding Decision Tree Data
Decision trees can be used when our data fulfill certain requirements. In this lesson, we learn what properties of a dataset allow us to create a decision tree.
Selecting Input Variables
Selecting Input Variables
Selecting the right input variables to include in a decision tree can be a difficult process. We'll learn how to approach this process in this lesson.
Growing the Tree
Growing the Tree
The first step in developing an effective decision tree is to grow the tree to a large size. We'll learn the principles of how this process works in this lesson.
Pruning the Tree
Pruning the Tree
After developing a large decision tree, we want to cut it back to a smaller size. We'll learn why we do this and the principles of how we do this in this lesson.
Analyzing the Decision Tree Structure
Analyzing the Decision Tree Structure
When you create a decision tree, you will see a number of different outputs. This lesson will explain what these outputs are and which are the most important.
Studying the Confusion Matrix
Studying the Confusion Matrix
The confusion matrix is a key tool for analyzing the accuracy of classification models and comparing different models to each other. This lesson introduces the concepts behind the confusion matrix and how to use it to calculate statistics that help us evaluate decision tree models.
Extending a Decision Tree
Extending a Decision Tree
Although decision trees are intuitive, a single decision tree is often not the best classification model. In this lesson, we learn why this is the case, and how to improve a decision tree model.
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