robability for the red class. Blue means a high
probability for the blue class. Here, clearly, it hasn't fit
the data perfectly. So I could add more and more models and
then, in the end, it would fit the data perfectly.

For multi-class, the default thing to do is One Versus Rest.

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class:spacious
# Gradient Boosting Advantages

# Early stopping

- Adding trees can lead to overfitting
- Stop adding trees when validation accuracy stops increasing

two choices:

- pick number of trees and tune learning rat