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Applications of Prediction Models Quiz

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Questions and Answers

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?

Data quality

Model performance

Feature importance

Class imbalance (correct)

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?

Measure of how successful the model is in identifying all negative instances as negative Signup and view all the answers

Which measure indicates the percentage of correct classified instances?

Accuracy Signup and view all the answers

What is F1-Score a measure of?

Precision and Recall balance Signup and view all the answers

What is the key difference between Classification and Regression in supervised learning?

Classification deals with categorical target variables, while Regression deals with continuous-valued numerical target variables Signup and view all the answers

Which component of a binary classification confusion matrix represents the cases where the model incorrectly predicted a 'Yes'?

False Positives Signup and view all the answers

What is the purpose of ROC and PRC graphs in classification models?

To evaluate how well a model performs at different classification thresholds Signup and view all the answers

Which of the following is an example of using a prediction model to classify data?

Classifying an email as spam or not spam Signup and view all the answers

When should Regression be used instead of Classification?

When predicting house prices based on historical data Signup and view all the answers

Which component of a binary classification confusion matrix represents the cases where the model correctly predicted a 'No'?

True Negatives Signup and view all the answers