Which evaluation metric is most appropriate for assessing the performance of an SVM model on an imbalanced dataset?
Practice Questions
1 question
Q1
Which evaluation metric is most appropriate for assessing the performance of an SVM model on an imbalanced dataset?
Accuracy
Precision
Recall
F1 Score
The F1 Score is a better evaluation metric for imbalanced datasets as it considers both precision and recall.
Questions & Step-by-step Solutions
1 item
Q
Q: Which evaluation metric is most appropriate for assessing the performance of an SVM model on an imbalanced dataset?
Solution: The F1 Score is a better evaluation metric for imbalanced datasets as it considers both precision and recall.
Steps: 7
Step 1: Understand what an imbalanced dataset is. An imbalanced dataset has a significant difference in the number of instances between different classes.
Step 2: Learn about evaluation metrics. Common metrics include accuracy, precision, recall, and F1 Score.
Step 3: Recognize that accuracy can be misleading in imbalanced datasets because a model can predict the majority class well but still perform poorly on the minority class.
Step 4: Understand precision, which measures how many of the predicted positive instances are actually positive.
Step 5: Understand recall, which measures how many actual positive instances were correctly predicted by the model.
Step 6: Learn that the F1 Score combines both precision and recall into one metric, providing a balance between the two.
Step 7: Conclude that the F1 Score is the most appropriate metric for assessing SVM model performance on imbalanced datasets because it accounts for both false positives and false negatives.
