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Machine Learning with scikit-learn Quick Start Guide

You're reading from   Machine Learning with scikit-learn Quick Start Guide Classification, regression, and clustering techniques in Python

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Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781789343700
Length 172 pages
Edition 1st Edition
Languages
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Author (1):
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Kevin Jolly Kevin Jolly
Author Profile Icon Kevin Jolly
Kevin Jolly
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Table of Contents (10) Chapters Close

Preface 1. Introducing Machine Learning with scikit-learn FREE CHAPTER 2. Predicting Categories with K-Nearest Neighbors 3. Predicting Categories with Logistic Regression 4. Predicting Categories with Naive Bayes and SVMs 5. Predicting Numeric Outcomes with Linear Regression 6. Classification and Regression with Trees 7. Clustering Data with Unsupervised Machine Learning 8. Performance Evaluation Methods 9. Other Books You May Enjoy

Understanding logistic regression mathematically

As the name implies, logistic regression is fundamentally derived from the linear regression algorithm. The linear regression algorithm will be discussed in depth in the upcoming chapters. For now, let's consider a hypothetical case in which we want to predict the probability that a particular loan will default based on the loan's interest rate. Using linear regression, the following equation can be constructed:

Default = (Interest Rate × x) + c

In the preceding equation, c is the intercept and x is a coefficient that will be the output from the logistic regression model. The intercept and the coefficient will have numeric values. For the purpose of this example, let's assume c is 5 and x is -0.2. The equation now becomes this:

Default = (Interest Rate × -0.2) + 5

The equation can be represented in a...

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