In Day 6, we saw how a Decision Tree Regressor finds its optimal split by minimizing the Mean Squared Error.
Today, for Day 7 of the Machine Learning “Advent Calendar”, we switch to classification. With just one numerical feature and two classes, we explore how a Decision Tree Classifier decides where to cut the data, using impurity measures like Gini and Entropy.
Even without doing the math, we can visually guess possible split points. But which one is best? And do impurity measures really make a difference? Let us build the first split step by step in Excel and see what happens.
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