Which evaluation metric is most appropriate for assessing the performance of a D
Practice Questions
Q1
Which evaluation metric is most appropriate for assessing the performance of a Decision Tree on a binary classification problem?
Mean Squared Error
Accuracy
Silhouette Score
R-squared
Questions & Step-by-Step Solutions
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.
