In a Decision Tree, what does the term 'Gini impurity' refer to?
Practice Questions
1 question
Q1
In a Decision Tree, what does the term 'Gini impurity' refer to?
A measure of the tree's depth
A metric for evaluating model performance
A criterion for splitting nodes
A method for pruning trees
Gini impurity is a criterion used to measure the impurity of a node, helping to determine the best feature to split on.
Questions & Step-by-step Solutions
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Q
Q: In a Decision Tree, what does the term 'Gini impurity' refer to?
Solution: Gini impurity is a criterion used to measure the impurity of a node, helping to determine the best feature to split on.
Steps: 6
Step 1: Understand that a Decision Tree is a tool used in machine learning to make decisions based on data.
Step 2: Know that a 'node' in a Decision Tree is a point where the data is split based on a feature.
Step 3: Learn that 'impurity' refers to how mixed or uncertain the data is at that node.
Step 4: Gini impurity is a specific way to measure this impurity, with a value between 0 and 1.
Step 5: A Gini impurity of 0 means all data points at that node belong to one class (pure), while a value closer to 1 means the data points are mixed (impure).
Step 6: The goal is to choose the feature that results in the lowest Gini impurity after the split, leading to more accurate predictions.
