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The Supervised Learning Workshop

You're reading from   The Supervised Learning Workshop Predict outcomes from data by building your own powerful predictive models with machine learning in Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781800209046
Length 532 pages
Edition 2nd Edition
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Authors (4):
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Blaine Bateman Blaine Bateman
Author Profile Icon Blaine Bateman
Blaine Bateman
Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
Ishita Mathur Ishita Mathur
Author Profile Icon Ishita Mathur
Ishita Mathur
Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
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Toc

3. Linear Regression

Overview

This chapter covers regression problems and analysis, introducing us to linear regression, as well as multiple linear regression and gradient descent. By the end of this chapter, you will be able to distinguish between regression and classification problems. You will be able to implement gradient descent in linear regression problems, and also apply it to other model architectures. You will also be able to use linear regression to construct a linear model for data in an x-y plane, evaluate the performance of linear models, and use the evaluation to choose the best model. In addition, you will be able to execute feature engineering to create dummy variables for constructing complicated linear models.

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