In the affinity analysis example, we looked for correlations between different variables in our dataset. In classification, we instead have a single variable that we are interested in and that we call the class (also called the target). If, in the previous example, we were interested in how to make people buy more apples, we could set that variable to be the class and look for classification rules that obtain that goal. We would then look only for rules that relate to that goal.
Learning Data Mining with Python
Learning Data Mining with Python
Overview of this book
Table of Contents (20 chapters)
Learning Data Mining with Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Getting Started with Data Mining
Classifying with scikit-learn Estimators
Predicting Sports Winners with Decision Trees
Recommending Movies Using Affinity Analysis
Extracting Features with Transformers
Social Media Insight Using Naive Bayes
Discovering Accounts to Follow Using Graph Mining
Beating CAPTCHAs with Neural Networks
Authorship Attribution
Clustering News Articles
Classifying Objects in Images Using Deep Learning
Working with Big Data
Next Steps…
Index
Customer Reviews