What is the difference between precision and recall?
Theme: Machine Learning Concepts Role: Machine Learning Engineer Function: Technology
Interview Question for Machine Learning Engineer: See sample answers, motivations & red flags for this common interview question. About Machine Learning Engineer: Builds machine learning models and algorithms. This role falls within the Technology function of a firm. See other interview questions & further information for this role here
Sample Answer
Example response for question delving into Machine Learning Concepts 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
- Goal: The goal of precision is to minimize false positive predictions, while the goal of recall is to minimize false negative predictions
- Trade-off: There is often a trade-off between precision and recall. Increasing one may decrease the other
- 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
- Evaluation: Precision is typically used when the focus is on positive predictions, while recall is used when the focus is on finding all positive instances
- Formula: Precision = TP / (TP + FP), where TP is true positives and FP is false positives. Recall = TP / (TP + FN), where FN is false negatives
Underlying Motivations
What the Interviewer is trying to find out about you and your experiences through this question
- Knowledge of machine learning concepts: Understanding the difference between precision and recall is fundamental in machine learning and demonstrates a solid understanding of the field
- Ability to evaluate model performance: Precision and recall are key metrics for evaluating the performance of machine learning models. The interviewer wants to assess your ability to interpret and use these metrics effectively
- Attention to detail: Precision and recall are nuanced metrics that require careful consideration. The interviewer may be interested in your attention to detail and ability to differentiate between these two metrics
Potential Minefields
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 calculation: Not being able to explain how precision and recall are calculated
- Inability to relate to real-world examples: Failing to provide examples or scenarios where precision and recall are applicable
- Not mentioning trade-off between precision & recall: Neglecting to discuss the trade-off between precision and recall and how it impacts model performance