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

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Multinomial logistic regression

Logistic regression would not be as useful if it only worked for binary classification problems. Fortunately, we can use multinomial logistic regression when our target has more than two values.

In this section, we will work with data on machine failures as a function of air and process temperature, torque, and rotational speed.

Note

This dataset on machine failure is available for public use at https://www.kaggle.com/datasets/shivamb/machine-predictive-maintenance-classification. There are 10,000 observations, 12 features, and two possible targets. One is binary – that is, the machine failed or didn’t. The other has types of failure. The instances in this dataset are synthetic, generated by a process designed to mimic machine failure rates and causes.

Let’s learn how to use multinomial logistic regression to model machine failure:

First, we will import the now-familiar libraries. We will also import cross_validate...

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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

Multinomial logistic regression

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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

Multinomial logistic regression

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