You're reading from Data Cleaning and Exploration with Machine Learning Get to grips with machine learning techniques to achieve sparkling-clean data quickly

Product type Paperback

Published in Aug 2022

Publisher Packt

ISBN-13 9781803241678

Length 542 pages

Edition 1st Edition

Concepts

Machine Learning

Author (1):

Michael Walker

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Table of Contents (23) Chapters

Preface

1. Section 1 – Data Cleaning and Machine Learning Algorithms

2. Chapter 1: Examining the Distribution of Features and Targets FREE CHAPTER

3. Chapter 2: Examining Bivariate and Multivariate Relationships between Features and Targets

4. Chapter 3: Identifying and Fixing Missing Values

5. Section 2 – Preprocessing, Feature Selection, and Sampling

6. Chapter 4: Encoding, Transforming, and Scaling Features

7. Chapter 5: Feature Selection

8. Chapter 6: Preparing for Model Evaluation

9. Section 3 – Modeling Continuous Targets with Supervised Learning

10. Chapter 7: Linear Regression Models

11. Chapter 8: Support Vector Regression

12. Chapter 9: K-Nearest Neighbors, Decision Tree, Random Forest, and Gradient Boosted Regression

13. Section 4 – Modeling Dichotomous and Multiclass Targets with Supervised Learning

14. Chapter 10: Logistic Regression

15. Chapter 11: Decision Trees and Random Forest Classification

16. Chapter 12: K-Nearest Neighbors for Classification

17. Chapter 13: Support Vector Machine Classification

18. Chapter 14: Naïve Bayes Classification

19. Section 5 – Clustering and Dimensionality Reduction with Unsupervised Learning

20. Chapter 15: Principal Component Analysis

21. Chapter 16: K-Means and DBSCAN Clustering

22. Other Books You May Enjoy

Identifying outliers and extreme values in bivariate relationships

It is hard to develop a reliable model without having a good sense of the bivariate relationships in our data. We not only care about the relationship between particular features and target variables but also about how features move together. If features are highly correlated, then modeling their independent effect becomes tricky or unnecessary. This may be a challenge, even if the features are highly correlated over just a range of values.

Having a good understanding of bivariate relationships is also important for identifying outliers. A value might be unexpected, even if it is not an extreme value. This is because some values for a feature are unusual when a second feature has certain values. This is easy to illustrate when one feature is categorical and the other is continuous.

The following diagram illustrates the number of bird sightings per day over several years but shows different distributions for the...

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You're reading from Data Cleaning and Exploration with Machine Learning Get to grips with machine learning techniques to achieve sparkling-clean data quickly

Table of Contents (23) Chapters

Identifying outliers and extreme values in bivariate relationships

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Table of Contents (23) Chapters

Identifying outliers and extreme values in bivariate relationships

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