![]() Overfitting is a problem that occurs when our algorithm is too closely aligned to our training data. The only objective the algorithm cares about is minimizing the gini impurity. it is completely “pure”.).Ĭ) Having an entire rule based upon this one observation seems silly, but it is perfectly logical at the moment. What outcome does your model predict?ī) What is the gini impurity of the final node, and why?Ĭ) Does the decision that led to this final node seem sensible to you? Why? AnswerĪ) From the top of the tree, we would work our way down:ī) This leads us to our single node with a gini impurity of 0. Using the image of the tree, work through the nodes until your can make a prediction. QuestionĪ) Consider a patient aged 45 years with an acute physiology score of 100. ![]() ![]() Looking at the tree, we can see that there are some very specific rules. create_graph ( mdl, feature_names = features ) Image ( graph.
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