Example response for question delving into Model Evaluation with the key points that need to be covered in an effective response. Customize this to your own experience with concrete examples and evidence

•  Definition: Precision is the ratio of true positive predictions to the total number of positive predictions. Recall is the ratio of true positive predictions to the total number of actual positive instances
•  Focus: Precision focuses on the accuracy of positive predictions, while recall focuses on the ability to find all positive instances
•  Use cases: Precision is important when the cost of false positives is high, such as in spam detection. Recall is important when the cost of false negatives is high, such as in disease diagnosis
•  Trade-off: There is often a trade-off between precision and recall. Increasing one usually leads to a decrease in the other
•  Evaluation: Precision is typically used in scenarios where the goal is to minimize false positives, while recall is used when the goal is to minimize false negatives
•  Formula: Precision = TP / (TP + FP), where TP is true positives and FP is false positives. Recall = TP / (TP + FN), where FN is false negatives

  What the Interviewer is trying to find out about you and your experiences through this question

•  Knowledge of key concepts: Understanding the fundamental concepts of precision and recall in data science
•  Analytical skills: Ability to differentiate and explain the nuances between precision and recall
•  Problem-solving abilities: Capability to apply precision and recall in real-world scenarios and optimize models accordingly
•  Communication skills: Effectively conveying the differences between precision and recall to non-technical stakeholders

  How to avoid some common minefields when answering this question in order to not raise any red flags

•  Confusing precision & recall: Mixing up the definitions or concepts of precision and recall
•  Lack of understanding of their purpose: Not being able to explain the importance or use cases of precision and recall
•  Inability to calculate or interpret precision & recall: Not being able to explain the formulas or interpret the results correctly
•  Not mentioning trade-off between precision & recall: Failing to discuss the trade-off between precision and recall and how it affects model performance

 Related

  Other questions asked for the Data Scientist in Technology function. View details for the Data Scientist here

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