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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

You're reading from  Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

Product type Book
Published in Jul 2020
Publisher Packt
ISBN-13 9781838826048
Pages 384 pages
Edition 1st Edition
Languages
Author (1):
Tarek Amr Tarek Amr
Profile icon Tarek Amr
Toc

Table of Contents (18) Chapters close

Preface 1. Section 1: Supervised Learning
2. Introduction to Machine Learning 3. Making Decisions with Trees 4. Making Decisions with Linear Equations 5. Preparing Your Data 6. Image Processing with Nearest Neighbors 7. Classifying Text Using Naive Bayes 8. Section 2: Advanced Supervised Learning
9. Neural Networks – Here Comes Deep Learning 10. Ensembles – When One Model Is Not Enough 11. The Y is as Important as the X 12. Imbalanced Learning – Not Even 1% Win the Lottery 13. Section 3: Unsupervised Learning and More
14. Clustering – Making Sense of Unlabeled Data 15. Anomaly Detection – Finding Outliers in Data 16. Recommender System – Getting to Know Their Taste 17. Other Books You May Enjoy

Using logistic regression for classification

"You can tell whether a man is clever by his answers. You can tell whether a man is wise by his questions."
Naguib Mahfouz

One day, when applying for a job, an interviewer asks: So tell me, is logistic regression a classification or a regression algorithm? The short answer to this is that it is a classification algorithm, but a longer and more interesting answer requires a good understanding of the logistic function. Then, the question may end up having a different meaning altogether.

Understanding the logistic function

The logistic function is a member of the sigmoid (s-shaped) functions, and it is represented by the following formula:

Don't let this equation scare you. What actually matters is how this function looks visually. Luckily, we can use our computer to generate a bunch of values for theta—for example, between -10 and 10....

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