O'Reilly logo

Data Analysis with R - Second Edition by Tony Fischetti

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Summary

At a high level, in this chapter you learned about four of the most popular classifiers out there: k-Nearest Neighbors, logistic regression, decision trees, and random forests. Not only did you learn the basics and mechanics of these four algorithms, but you saw how easy they were to perform in R. Along the way, you learned about confusion matrices, hyper-parameter tuning, and maybe even a few new R incantations.

We also visited some more general ideas; for example, you've expanded your understanding of the bias-variance trade-off, looked at how the GLM can perform great feats, and have become acquainted with ensemble learning and bootstrap aggregation. It's also my hope that you've developed some intuition as to which classifiers ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required