What does the Gini impurity measure in a Decision Tree?

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Question: What does the Gini impurity measure in a Decision Tree?

Options:

Correct Answer: The likelihood of misclassifying a randomly chosen element.

Solution:

Gini impurity measures the likelihood of misclassifying a randomly chosen element from the dataset, helping to determine the best splits.

What does the Gini impurity measure in a Decision Tree?

What does the Gini impurity measure in a Decision Tree?

Step 1: Understand that Gini impurity is a way to measure how mixed the classes are in a dataset.
Step 2: Realize that a lower Gini impurity means the classes are more pure (less mixed), while a higher Gini impurity means the classes are more mixed.
Step 3: Know that Gini impurity helps in decision trees by showing how likely it is to misclassify a randomly chosen item from the dataset.
Step 4: Use Gini impurity to find the best way to split the data into groups that are more similar to each other.

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