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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
Author Profile Icon Dr. Samuel Asare
Dr. Samuel Asare
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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

Random Search

Instead of searching through every hyperparameterizations in a pre-defined set, as is the case with a grid search, in a random search we sample from a distribution of possibilities by assuming each hyperparameter to be a random variable. Before we go through the process in depth, it will be helpful to briefly review what random variables are and what we mean by a distribution.

Random Variables and Their Distributions

A random variable is non-constant (its value can change) and its variability can be described in terms of distribution. There are many different types of distributions, but each falls into one of two broad categories: discrete and continuous. We use discrete distributions to describe random variables whose values can take only whole numbers, such as counts.

An example is the count of visitors to a theme park in a day, or the number of attempted shots it takes a golfer to get a hole-in-one.

We use continuous distributions to describe...

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