Skip to Main Content
Data Science Using Python and R
book

Data Science Using Python and R

by Chantal D. Larose, Daniel T. Larose
April 2019
Beginner to intermediate content levelBeginner to intermediate
240 pages
6h 47m
English
Wiley
Content preview from Data Science Using Python and R

Chapter 11REGRESSION MODELING

11.1 THE ESTIMATION TASK

Thus far in the Modeling Phase we have covered the following tasks:

  • Classification task
  • Clustering task

There remain two tasks left to cover:

  • Estimation task
  • Association task

In this chapter, we cover the estimation task; later, in Chapter 14, we will cover the association task.

The most widespread method for performing the estimation task is linear regression. Simple linear regression approximates the relationship between a numeric predictor and a continuous target, using a straight line. Multiple regression modeling approximates the relationship between a set of p > 1 predictors and a single continuous target, using a p‐dimensional plane or hyperplane.

11.2 DESCRIPTIVE REGRESSION MODELING

The usual multiple regression model is a parametric model, defined by the following equation:

equation

where the x's represent the predictor variables, and the β's represent the unknown model parameters, whose values are estimated using the data.1 Now, estimating model parameters using sample data represents classical statistical inference. The Data Science Methodology outlined in Chapter 1, however, employs cross‐validation rather than classical statistical inference to validate model results. Thus, in this book, we will bypass the parametric regression equation above, in favor of a descriptive approach to regression modeling, using the ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Practical Data Science with Python 3: Synthesizing Actionable Insights from Data

Practical Data Science with Python 3: Synthesizing Actionable Insights from Data

Ervin Varga
Python Data Science Essentials - Third Edition

Python Data Science Essentials - Third Edition

Alberto Boschetti, Luca Massaron, Pietro Marinelli, Matteo Malosetti

Publisher Resources

ISBN: 9781119526810Purchase book