Which evaluation metric is most appropriate for assessing the performance of an
Practice Questions
Q1
Which evaluation metric is most appropriate for assessing the performance of an SVM model on imbalanced datasets?
Accuracy
Precision
Recall
F1 Score
Questions & Step-by-Step Solutions
Which evaluation metric is most appropriate for assessing the performance of an SVM model on imbalanced datasets?
Step 1: Understand what an SVM model is. SVM stands for Support Vector Machine, which is a type of machine learning model used for classification tasks.
Step 2: Learn about imbalanced datasets. An imbalanced dataset is when one class (category) has many more examples than the other class.
Step 3: Know the common evaluation metrics. Common metrics include accuracy, precision, recall, and F1 score.
Step 4: Understand precision. Precision measures how many of the predicted positive cases were actually positive.
Step 5: Understand recall. Recall measures how many of the actual positive cases were correctly predicted by the model.
Step 6: Learn about the F1 Score. The F1 Score is the harmonic mean of precision and recall, which means it takes both into account.
Step 7: Realize why F1 Score is better for imbalanced datasets. In imbalanced datasets, accuracy can be misleading because a model might predict the majority class well but fail on the minority class. F1 Score provides a better balance between precision and recall, making it more suitable.
