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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
Author Profile Icon Robert Thas John
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
Author Profile Icon Andrew Worsley
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

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 random variables...

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