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The Data Science Workshop

You're reading from   The Data Science Workshop A New, Interactive Approach to Learning Data Science

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Product type Paperback
Published in Jan 2020
Publisher
ISBN-13 9781838981266
Length 818 pages
Edition 1st Edition
Languages
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Authors (5):
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Thomas Joseph Thomas Joseph
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Thomas Joseph
Andrew Worsley Andrew Worsley
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Andrew Worsley
Robert Thas John Robert Thas John
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Robert Thas John
Anthony So Anthony So
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Anthony So
Dr. Samuel Asare Dr. Samuel Asare
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Dr. Samuel Asare
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Toc

Table of Contents (18) Chapters Close

Preface 1. Introduction to Data Science in Python 2. Regression FREE CHAPTER 3. Binary Classification 4. Multiclass Classification with RandomForest 5. Performing Your First Cluster Analysis 6. How to Assess Performance 7. The Generalization of Machine Learning Models 8. Hyperparameter Tuning 9. Interpreting a Machine Learning Model 10. Analyzing a Dataset 11. Data Preparation 12. Feature Engineering 13. Imbalanced Datasets 14. Dimensionality Reduction 15. Ensemble Learning 16. Machine Learning Pipelines 17. Automated Feature Engineering

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.

In the data science industry, quite often you will face multiclass classification problems. For example, if you were working for Netflix or any other streaming platform, you would have to build a model that could predict the user rating for a movie based on key attributes such as genre, duration, or cast. A potential list of rating values may be: Hate it, Dislike it, Neutral, Like it, Love it. The objective of the model would be to predict the right rating from those five possible values.

Multiclass classification doesn't always mean the response variable will be text. In some datasets, the target variable may be encoded...

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