O'Reilly logo

R: Data Analysis and Visualization by Ágnes Vidovics-Dancs, Kata Váradi, Tamás Vadász, Ágnes Tuza, Balázs Árpád Szucs, Julia Molnár, Péter Medvegyev, Balázs Márkus, István Margitai, Péter Juhász, Dániel Havran, Gergely Gabler, Barbara Dömötör, Gergely Daróczi, Ádám Banai, Milán Badics, Ferenc Illés, Edina Berlinger, Bater Makhabel, Hrishi V. Mittal, Jaynal Abedin, Brett Lantz, 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

Decision trees

We now move on to one of the easily interpretable and most popular classifiers there are out there: the decision tree. Decision trees—which look like an upside down tree with the trunk on top and the leaves on the bottom—play an important role in situations where classification decisions have to be transparent and easily understood and explained. It also handles both continuous and categorical predictors, outliers, and irrelevant predictors rather gracefully. Finally, the general ideas behind the algorithms that create decision trees are quite intuitive, though the details can sometimes get hairy.

Figure 9.7 depicts a simple decision tree designed to classify motor vehicles into either motorcycles, golf carts, or sedans.

Figure 9.7: ...

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