Skip to Content
Hands-On Automated Machine Learning
book

Hands-On Automated Machine Learning

by Sibanjan Das, Umit Mert Cakmak
April 2018
Beginner to intermediate content levelBeginner to intermediate
282 pages
6h 52m
English
Packt Publishing
Content preview from Hands-On Automated Machine Learning

The decision boundary of random forest

Random forest is a meta estimator, that will build many different models and aggregate their predictions to come up with a final prediction. Random forest is able to produce non-linear decision boundaries, since there's no linear relationship between inputs and outputs. It has many hyperparameters to play with but for the sake of simplicity, you will use the default configuration:

from sklearn.ensemble import RandomForestClassifierdraw_decision_boundary(RandomForestClassifier(), X, y)

We get the following plot from the preceding code:

Not looking too bad at all! Every algorithm will provide you with different ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Automated Machine Learning

Automated Machine Learning

Adnan Masood
R: Unleash Machine Learning Techniques

R: Unleash Machine Learning Techniques

Raghav Bali, Dipanjan Sarkar, Brett Lantz, Cory Lesmeister

Publisher Resources

ISBN: 9781788629898Supplemental Content