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Practical Data Science with Python

You're reading from   Practical Data Science with Python Learn tools and techniques from hands-on examples to extract insights from data

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781801071970
Length 620 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Nathan George Nathan George
Author Profile Icon Nathan George
Nathan George
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Table of Contents (30) Chapters Close

Preface 1. Part I - An Introduction and the Basics
2. Introduction to Data Science FREE CHAPTER 3. Getting Started with Python 4. Part II - Dealing with Data
5. SQL and Built-in File Handling Modules in Python 6. Loading and Wrangling Data with Pandas and NumPy 7. Exploratory Data Analysis and Visualization 8. Data Wrangling Documents and Spreadsheets 9. Web Scraping 10. Part III - Statistics for Data Science
11. Probability, Distributions, and Sampling 12. Statistical Testing for Data Science 13. Part IV - Machine Learning
14. Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction 15. Machine Learning for Classification 16. Evaluating Machine Learning Classification Models and Sampling for Classification 17. Machine Learning with Regression 18. Optimizing Models and Using AutoML 19. Tree-Based Machine Learning Models 20. Support Vector Machine (SVM) Machine Learning Models 21. Part V - Text Analysis and Reporting
22. Clustering with Machine Learning 23. Working with Text 24. Part VI - Wrapping Up
25. Data Storytelling and Automated Reporting/Dashboarding 26. Ethics and Privacy 27. Staying Up to Date and the Future of Data Science 28. Other Books You May Enjoy
29. Index

Optimizing Models and Using AutoML

So far, we've looked at a few machine learning (ML) models for classification and regression: simple linear models (linear regression and logistic regression), k-nearest neighbors (KNN), and Naïve Bayes for classification. As we will see in these next few chapters, there are other models that are commonly used in ML and data science. This chapter will cover how to choose between models and how to optimize models. Specifically, we'll cover:

  • Hyperparameter optimization with random, grid, and Bayesian searches
  • Using learning curves to optimize the amount of data needed and diagnose ML models
  • Optimizing the number of features with recursive feature selection
  • Using the pycaret AutoML Python package

Let's get started with hyperparameter optimization using a few different search methods.

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