Which evaluation metric is commonly used for assessing the performance of a Decision Tree classifier?
Practice Questions
1 question
Q1
Which evaluation metric is commonly used for assessing the performance of a Decision Tree classifier?
Mean absolute error
F1 score
R-squared
Root mean squared error
The F1 score is commonly used for classification tasks to balance precision and recall.
Questions & Step-by-step Solutions
1 item
Q
Q: Which evaluation metric is commonly used for assessing the performance of a Decision Tree classifier?
Solution: The F1 score is commonly used for classification tasks to balance precision and recall.
Steps: 5
Step 1: Understand that a Decision Tree classifier is a type of model used for classification tasks.
Step 2: Know that when we evaluate how well a classifier is performing, we need metrics to measure its effectiveness.
Step 3: Learn about common evaluation metrics, such as accuracy, precision, recall, and F1 score.
Step 4: Recognize that the F1 score is particularly useful because it combines both precision (how many selected items are relevant) and recall (how many relevant items are selected).
Step 5: Conclude that the F1 score is commonly used for assessing the performance of a Decision Tree classifier.
