Which evaluation metric is best suited for imbalanced classification problems?
Practice Questions
1 question
Q1
Which evaluation metric is best suited for imbalanced classification problems?
Accuracy
F1 Score
Mean Squared Error
R-squared
F1 Score is better for imbalanced datasets as it considers both precision and recall.
Questions & Step-by-step Solutions
1 item
Q
Q: Which evaluation metric is best suited for imbalanced classification problems?
Solution: F1 Score is better for imbalanced datasets as it considers both precision and recall.
Steps: 5
Step 1: Understand what imbalanced classification means. This is when one class has many more examples than the other class.
Step 2: Learn about evaluation metrics. These are ways to measure how well a model is performing.
Step 3: Know that common metrics like accuracy can be misleading in imbalanced datasets because they may not reflect the true performance.
Step 4: Discover the F1 Score. It combines two important metrics: precision (how many selected items are relevant) and recall (how many relevant items are selected).
Step 5: Realize that the F1 Score is useful because it balances precision and recall, making it a better choice for imbalanced datasets.
