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Plot of the first decision tree in our random forests model (index 0) showing the Gini impurity index in each node. The Gini impurity is a metric that gives the probability of an incorrect classification of a randomly selected test datum from our labeled fall dataset, if the selected test datum was randomly labeled in either the fall or no-fall class.
