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In a binary classification problem, what does a high precision indicate?

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Question: In a binary classification problem, what does a high precision indicate?

Options:

  1. High true positive rate
  2. Low false positive rate
  3. High true negative rate
  4. Low false negative rate

Correct Answer: Low false positive rate

Solution: 

High precision indicates that when the model predicts a positive class, it is correct most of the time, meaning low false positives.

In a binary classification problem, what does a high precision indicate?

Practice Questions

Q1
In a binary classification problem, what does a high precision indicate?
  1. High true positive rate
  2. Low false positive rate
  3. High true negative rate
  4. Low false negative rate

Questions & Step-by-Step Solutions

In a binary classification problem, what does a high precision indicate?
  • Step 1: Understand what binary classification means. It involves two classes: positive and negative.
  • Step 2: Learn what precision is. Precision measures how many of the predicted positive cases are actually positive.
  • Step 3: Realize that high precision means that when the model predicts a positive case, it is usually correct.
  • Step 4: Understand that high precision also means there are few false positives, which are incorrect positive predictions.
  • Precision – Precision is the ratio of true positive predictions to the total predicted positives, indicating the accuracy of positive predictions.
  • Binary Classification – A type of classification problem where there are only two possible classes or outcomes.
  • False Positives – Instances where the model incorrectly predicts the positive class when the actual class is negative.
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