A recommendation engine uses a decision tree to predict movie preferences. Which outcome occurs when the splitting criteria solely minimizes Gini impurity at each node?

💡 Explanation

The decision tree tends to favor splitting on features with numerous categories when using Gini impurity because the information gain is higher for these features; therefore, the tree becomes biased rather than optimizing the predictive accuracy, because it can overfit quickly if not pruned.

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