Building Predictive Models Using Penalized Linear Methods

This chapter discusses building predictive models using penalized linear methods. It demonstrates the use of penalized regression along with a number of general tools for predictive modeling. The chapter utilizes Python packages incarnating various different flavors of penalized regression for these...

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Bibliographic Details
Published inMachine Learning with Spark and Python pp. 1 - 2
Main Author Bowles, Michael
Format Book Chapter
LanguageEnglish
Published United States John Wiley & Sons 2020
John Wiley & Sons, Incorporated
John Wiley & Sons, Inc
Edition2nd Edition
Subjects
binary classification
building predictive models
General Engineering & Project Administration
General References
multiclass classification
penalized linear methods
predicting wine taste
PySpark logistic regression
Python packages
Online AccessGet full text
ISBN1119561930
9781119561934
DOI10.1002/9781119562023.ch5

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Table of Contents:
  • 5.1 Python Packages for Penalized Linear Regression 5.2 Multivariable Regression: Predicting Wine Taste 5.3 Binary Classification: Using Penalized Linear Regression to Detect Unexploded Mines 5.4 Multiclass Classification: Classifying Crime Scene Glass Samples 5.5 Linear Regression and Classification Using PySpark 5.6 Using PySpark to Predict Wine Taste 5.7 Logistic Regression with PySpark: Rocks versus Mines 5.8 Incorporating Categorical Variables in a PySpark Model: Predicting Abalone Rings 5.9 Multiclass Logistic Regression with Meta Parameter Optimization 5.10 Summary References