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Which metric is most appropriate when evaluating a regression model used to predict housing prices?

A) PrecisionB) RecallC) RMSED) Accuracy

A healthcare startup is using a model to predict whether a patient has a rare disease. Which metric should they prioritise?

A) PrecisionB) AccuracyC) RecallD) MAE

Which of the following metrics is most appropriate for evaluating a multiclass classification model that predicts customer sentiment as positive, neutral, or negative?

A) RMSEB) AccuracyC) Macro-average F1-scoreD) MAE

Your fraud detection model flags legitimate users too often, leading to customer complaints. What should you improve?

A) RecallB) PrecisionC) AccuracyD) R²

Which of the following best reflects the purpose of business-aligned metric selection?

A) To identify the model with the lowest computational cost.B) To evaluate model performance without considering outcomes.C) To ensure the model's evaluation focuses on errors that matter most to the business.D) To create a universal benchmark for all use cases.

A logistics company is more concerned about large errors than small ones when predicting delivery time. Which metric is most appropriate?

A) MAEB) RMSEC) AccuracyD) Precision

Why might a business opt to implement a custom threshold for classification?

A) To simplify metric calculation.B) To reduce the time it takes to train the model.C) To shift the model's balance between false positives and false negatives.D) To reduce overfitting on the training set.

Which of the following statements best describes the role of metric implementation in production environments?

A) Metrics are only useful during initial model training.B) Evaluation metrics should be replaced with business KPIs after deployment.C) Metric tracking continues post-deployment to monitor ongoing model effectiveness.D) Once the model reaches high accuracy, ongoing evaluation is unnecessary.

Which of the following scenarios would justify switching from accuracy to F1-score as your primary metric?

A) Your dataset is balanced, and all classes are equally important.B) Your model is used only internally, and its predictions don't influence any decisions.C) You're working with imbalanced data, and both false positives and false negatives carry business costs.D) You're optimising for interpretability over performance.

How should you approach metric selection when the business goal changes mid-project?

A) Stick with the original metric to maintain consistency.B) Immediately retrain the model with new features.C) Reassess the cost of model errors and update your evaluation criteria accordingly.D) Replace classification with regression.

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