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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
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Thomas Joseph
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
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

Random State

The key to reproducing the same results is called random state. You simply specify a number, and whenever that number is used, the same results will be produced. This works because computers don't have an actual random number generator. Instead, they have a pseudo-random number generator. This means that you can generate the same sequence of random numbers if you set a random state.

Consider the following figure as an example. The columns are your random states. If you pick 0 as the random state, the following numbers will be generated: 41, 52, 38, 56…

However, if you pick 1 as the random state, a different set of numbers will be generated, and so on.

Figure 7.10: Numbers generated using random state

In the previous exercise, you set the random state to 0 so that the experiment was repeatable.

Exercise 7.02: Setting a Random State When Splitting Data

The goal of this exercise is to have a reproducible way of splitting...

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