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The Data Science Workshop

You're reading from   The Data Science Workshop Learn how you can build machine learning models and create your own real-world data science projects

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781800566927
Length 824 pages
Edition 2nd Edition
Languages
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Authors (5):
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Robert Thas John Robert Thas John
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Robert Thas John
Thomas Joseph Thomas Joseph
Author Profile Icon Thomas Joseph
Thomas Joseph
Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
Dr. Samuel Asare Dr. Samuel Asare
Author Profile Icon Dr. Samuel Asare
Dr. Samuel Asare
Andrew Worsley Andrew Worsley
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Andrew Worsley
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Toc

Table of Contents (16) 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

Scikit-Learn

Scikit-learn (also referred to as sklearn) is another extremely popular package used by data scientists. The main purpose of sklearn is to provide APIs for processing data and training machine learning algorithms. But before moving ahead, we need to know what a model is.

What Is a Model?

A machine learning model learns patterns from data and creates a mathematical function to generate predictions. A supervised learning algorithm will try to find the relationship between a response variable and the given features.

Have a look at the following example.

A mathematical function can be represented as a function, Æ’(), that is applied to some input variables, X (which is composed of multiple features), and will calculate an output (or prediction), Å·:

Figure 1.37: Function f(X)

The function, Æ’(), can be quite complex and have different numbers of parameters. If we take a linear regression (this will be presented in more detail...

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