What evaluation metrics would you use for a classification problem?

 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

  •  Accuracy: Accuracy is a commonly used metric for classification problems. It measures the proportion of correctly classified instances out of the total number of instances
  •  Precision: Precision is the ratio of true positive predictions to the total number of positive predictions. It indicates the model's ability to correctly identify positive instances
  •  Recall: Recall, also known as sensitivity or true positive rate, measures the proportion of true positive predictions out of the total number of actual positive instances. It indicates the model's ability to find all positive instances
  •  F1 Score: The F1 score is the harmonic mean of precision and recall. It provides a balanced measure of a model's performance by considering both precision and recall
  •  Confusion Matrix: A confusion matrix provides a detailed breakdown of the model's predictions. It shows the number of true positives, true negatives, false positives, and false negatives, allowing for a deeper analysis of the model's performance
  •  Receiver Operating Characteristic (ROC) Curve: The ROC curve is a graphical representation of the model's performance across different classification thresholds. It plots the true positive rate against the false positive rate, providing insights into the trade-off between sensitivity and specificity
  •  Area Under the ROC Curve (AUC): The AUC is a single scalar value that summarizes the overall performance of a model across all possible classification thresholds. It ranges from 0 to 1, with higher values indicating better performance
  •  Log Loss: Log loss, also known as cross-entropy loss, measures the performance of a classification model that outputs probabilities. It quantifies the difference between predicted probabilities and the true class labels
  •  Classification Report: A classification report provides a comprehensive summary of various evaluation metrics, including precision, recall, F1 score, and support (number of instances) for each class. It helps assess the model's performance on individual classes
  •  Other Metrics: Depending on the specific problem and requirements, other evaluation metrics such as Cohen's kappa, Matthews correlation coefficient, or area under the precision-recall curve may also be relevant
  •  Considerations: When selecting evaluation metrics, it is important to consider the problem domain, class imbalance, cost of misclassification, and specific objectives of the classification task

 Underlying Motivations 


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

  •  Knowledge of evaluation metrics: Assessing if the candidate understands different evaluation metrics and their appropriate use in classification problems
  •  Problem-solving skills: Evaluating the candidate's ability to select and justify the choice of evaluation metrics based on the problem at hand
  •  Understanding of model performance: Determining if the candidate can effectively measure and interpret the performance of classification models using appropriate evaluation metrics

 Potential Minefields 


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

  •  Lack of knowledge: Not being able to mention any evaluation metrics for classification problems
  •  Vague or generic answer: Providing a general response without specifying any specific evaluation metrics for classification problems
  •  Inability to explain metrics: Not being able to explain the purpose or calculation of the mentioned evaluation metrics
  •  Ignoring trade-offs: Failing to mention the trade-offs associated with different evaluation metrics and their suitability for specific scenarios
  •  Limited understanding: Showing a limited understanding of evaluation metrics commonly used in classification problems, such as accuracy, precision, recall, F1 score, and ROC-AUC

 Related

  Other questions asked for the Machine Learning Engineer in Technology function. View details for the Machine Learning Engineer here