Using Trees for Predictive Analysis

Learning Objectives

By the end of this chapter, you will be able to:

  • Understand the metrics used for evaluating the utility of a data model
  • Classify datapoints based on decision trees
  • Classify datapoints based on the random forest algorithm

In this chapter, we will learn about two types of supervised learning algorithm in detail. The first algorithm will help us to classify data points using decision trees, while the other algorithm will help us classify using random forests.

Introduction to Decision Trees

In decision trees, we have input and corresponding output in the training data. A decision tree, like any tree, has leaves, branches, and nodes. Leaves are the end nodes like a yes or no. Nodes ...

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