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

Product type Paperback

Published in Sep 2021

Publisher Packt

ISBN-13 9781801071970

Length 620 pages

Edition 1st Edition

Languages

Python

Tools

Excel

Concepts

Data Science

Author (1):

Nathan George

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Table of Contents (30) Chapters

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

Using AutoML with PyCaret

So far, we've looked at a few different ML models. However, there are many more, and it can be tedious to try many of them by hand. An easier way to try many models at once is with automated machine learning, or AutoML.

The no free lunch theorem

In ML, we usually don't know which model will perform best. Take our logistic regression models and Naïve Bayes, and the logistic regression models from Chapter 11, Machine Learning for Classification, on classification. We didn't have too many reasons to know which one might perform best before trying them. Of course, we know the Gaussian Naïve Bayes assumes features have a normal distribution, which seemed wrong, so we might guess that model may not work well. We can use assumptions for models to guess which models may or may not work, but beyond that, we should try several different models and compare the results, then choose the best-performing model based on the model evaluation...