Which of the following statements about the bias-variance trade-off in Decision Trees is most accurate, particularly when considering model complexity?
Question 2
In a Decision Tree, what is the primary purpose of using Gini impurity as a splitting criterion for a classification task?
Question 3
Consider a Decision Tree used for regression. If a node has a Mean Squared Error (MSE) of $40$, and a potential split results in two child nodes with MSEs of $15$ (containing 60% of the data) and $20$ (containing 40% of the data) respectively, what is the reduction in MSE achieved by this split?
Question 4
When dealing with categorical features in Decision Tree construction, which of the following is a common approach to determine the optimal split?
Question 5
Which of the following scenarios would most likely necessitate the use of pruning strategies in a Decision Tree to improve its generalization performance?
