Podcast
Questions and Answers
When should ROC curves be used?
When should ROC curves be used?
- When there is a moderate to large class imbalance
- When there are more positives than negatives
- When there are more negatives than positives
- When there are roughly equal numbers of observations for each class (correct)
What does Precision-Recall curves indicate?
What does Precision-Recall curves indicate?
- Data quality
- Model performance
- Feature importance
- Class imbalance (correct)
What does Recall measure?
What does Recall measure?
- Percentage of correct classified instances
- How successful the model is in identifying all positive instances as positive (correct)
- Measure of how successful the model is in identifying all negative instances as negative
- Percentage of instances classified as 'Yes' that were actually 'Yes'
What does Specificity measure?
What does Specificity measure?
Which measure indicates the percentage of correct classified instances?
Which measure indicates the percentage of correct classified instances?
What is F1-Score a measure of?
What is F1-Score a measure of?
What is the key difference between Classification and Regression in supervised learning?
What is the key difference between Classification and Regression in supervised learning?
Which component of a binary classification confusion matrix represents the cases where the model incorrectly predicted a 'Yes'?
Which component of a binary classification confusion matrix represents the cases where the model incorrectly predicted a 'Yes'?
What is the purpose of ROC and PRC graphs in classification models?
What is the purpose of ROC and PRC graphs in classification models?
Which of the following is an example of using a prediction model to classify data?
Which of the following is an example of using a prediction model to classify data?
When should Regression be used instead of Classification?
When should Regression be used instead of Classification?
Which component of a binary classification confusion matrix represents the cases where the model correctly predicted a 'No'?
Which component of a binary classification confusion matrix represents the cases where the model correctly predicted a 'No'?
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