Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners by Scott Hartshorn

Author:Scott Hartshorn [Hartshorn, Scott]
Language: eng
Format: azw3, epub, pdf
Published: 2016-08-11T16:00:00+00:00

Criteria Selection

The other way that a random forest adds randomness to a decision tree is deciding which feature to split the tree on. Within any given feature, the split will be located at the location which maximizes the information gain on the tree, i.e. the best location. Additionally, if the decision tree evaluates multiple features, it will pick the best location in all the features that it looks at when deciding where to make the split. So if all of the trees looked at the same features, they would be very similar.

The way that Random Forest deals with that is to not let the trees look at all of the features. At any given branch in the decision tree, only a subset of the features are available for it to classify on. Other branches, even higher or lower branches on the same tree, will have different features that they can classify on.

For example, let’s say that you have a Random Forest trying to classify fruit into either pears or apples. You make the Random Forest with 2 decision trees in it, and you pass it 4 features that it can classify on

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