Which evaluation metric is most appropriate for assessing the performance of a D

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Question: Which evaluation metric is most appropriate for assessing the performance of a Decision Tree on a binary classification problem?

Options:

Correct Answer: Accuracy

Solution:

Accuracy is a common metric for evaluating the performance of classification models, including Decision Trees.

Which evaluation metric is most appropriate for assessing the performance of a Decision Tree on a binary classification problem?

Which evaluation metric is most appropriate for assessing the performance of a Decision Tree on a binary classification problem?

Step 1: Understand that a Decision Tree is a model used for classification tasks, which means it predicts categories or classes.
Step 2: Recognize that in a binary classification problem, there are two classes (e.g., yes/no, true/false).
Step 3: Learn about evaluation metrics, which are tools used to measure how well a model performs.
Step 4: Identify that accuracy is a common metric, calculated as the number of correct predictions divided by the total number of predictions.
Step 5: Conclude that accuracy is appropriate for assessing the performance of a Decision Tree in a binary classification problem, as it gives a straightforward measure of how many predictions were correct.

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