The Data Science Workshop

4. Multiclass Classification with RandomForest

Overview

This chapter will show you how to train a multiclass classifier using the Random Forest algorithm. You will also see how to evaluate the performance of multiclass models. By the end of the chapter, you will be able to tune the key hyperparameters of Random Forest and split data into training/testing sets.

Introduction

In the previous chapter, you saw how to build a binary classifier using the famous Logistic Regression algorithm. A binary classifier can only take two different values for its response variables, such as 0 and 1 or yes and no. A multiclass classification task is just an extension. Its response variable can have more than two different values.

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