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Which evaluation metric is most appropriate for a binary classification problem?

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Question: Which evaluation metric is most appropriate for a binary classification problem?

Options:

  1. Mean Squared Error
  2. Accuracy
  3. Silhouette Score
  4. R-squared

Correct Answer: Accuracy

Solution: 

Accuracy is a common evaluation metric for binary classification problems, measuring the proportion of correct predictions.

Which evaluation metric is most appropriate for a binary classification problem?

Practice Questions

Q1
Which evaluation metric is most appropriate for a binary classification problem?
  1. Mean Squared Error
  2. Accuracy
  3. Silhouette Score
  4. R-squared

Questions & Step-by-Step Solutions

Which evaluation metric is most appropriate for a binary classification problem?
  • Step 1: Understand that a binary classification problem involves two classes, such as 'yes' and 'no'.
  • Step 2: Learn that accuracy measures how many predictions were correct out of all predictions made.
  • Step 3: Calculate accuracy by using the formula: (Number of correct predictions) / (Total number of predictions).
  • Step 4: Recognize that while accuracy is useful, it may not always be the best metric, especially if the classes are imbalanced.
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