In this chapter, we looked at using probabilistic linear models to predict a qualitative response with the two most common methods: logistic regression and discriminant analysis. Additionally, we began the process of using the ROC charts in order to explore the model selection visually and statistically. We also briefly discussed the model selection and trade-offs that you need to consider. In the future chapters, we will revisit the breast cancer dataset to see if we can improve our predictive ability with more complex techniques.
Mastering Machine Learning with R
By :
Mastering Machine Learning with R
By:
Overview of this book
Table of Contents (20 chapters)
Mastering Machine Learning with R
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
A Process for Success
Linear Regression – The Blocking and Tackling of Machine Learning
Logistic Regression and Discriminant Analysis
Advanced Feature Selection in Linear Models
More Classification Techniques – K-Nearest Neighbors and Support Vector Machines
Classification and Regression Trees
Neural Networks
Cluster Analysis
Principal Components Analysis
Market Basket Analysis and Recommendation Engines
Time Series and Causality
Text Mining
R Fundamentals
Index
Customer Reviews