1. What is the Q Score?

The Q Score is a statistical measure that calculates the proportion of correct classifications in a binary classification system. It represents the accuracy of a test or model by combining true positives and true negatives relative to the total number of cases.

2. How Does the Calculator Work?

The calculator uses the Q Score formula:

Where:

• \( true\ positives \) — Number of correctly identified positive cases
• \( true\ negatives \) — Number of correctly identified negative cases
• \( total \) — Total number of cases

Explanation: The Q Score provides a simple measure of overall accuracy in classification tasks, ranging from 0 to 1, where 1 represents perfect classification.

3. Importance of Q Score Calculation

Details: The Q Score is important for evaluating the performance of binary classification systems, including medical tests, machine learning models, and quality control processes. It provides a straightforward measure of overall accuracy.

4. Using the Calculator

Tips: Enter the number of true positives, true negatives, and total cases. All values must be non-negative integers, and the sum of true positives and true negatives should not exceed the total number of cases.

5. Frequently Asked Questions (FAQ)

Q1: What is the range of Q Score values?
A: Q Score ranges from 0 to 1, where 0 indicates no correct classifications and 1 indicates perfect classification accuracy.

Q2: How does Q Score differ from accuracy?
A: Q Score is essentially the same as accuracy in binary classification - it measures the proportion of correct predictions among all predictions.

Q3: When should I use Q Score?
A: Use Q Score when you need a simple measure of overall classification performance, particularly when both false positives and false negatives are equally important.

Q4: What are the limitations of Q Score?
A: Q Score doesn't distinguish between types of errors (false positives vs false negatives) and can be misleading with imbalanced datasets where one class dominates.

Q5: Can Q Score be used for multi-class classification?
A: While primarily designed for binary classification, the Q Score concept can be extended to multi-class problems by considering correct classifications across all classes.