April 2018
Beginner to intermediate
282 pages
6h 52m
English
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 ...