1. What is Accuracy?

Accuracy is a statistical measure that evaluates how often a classification model makes correct predictions. It's the ratio of correct predictions (both true positives and true negatives) to the total number of cases examined.

2. How Does the Calculator Work?

The calculator uses the accuracy formula:

Where:

• \( true\_positive \) — Correctly identified positive cases
• \( true\_negative \) — Correctly identified negative cases
• \( all \) — Total number of cases examined

Explanation: The formula calculates the proportion of correct predictions among all predictions made by the model.

3. Importance of Accuracy Calculation

Details: Accuracy is a fundamental metric for evaluating classification models, though it should be considered alongside other metrics like precision and recall, especially with imbalanced datasets.

4. Using the Calculator

Tips: Enter the number of true positives, true negatives, and total cases. All values must be non-negative integers, and total cases must be greater than zero.

5. Frequently Asked Questions (FAQ)

Q1: What is a good accuracy score?
A: Generally, higher is better, but interpretation depends on context. For balanced binary classification, accuracy above 0.8 is often considered good.

Q2: When is accuracy not a good metric?
A: Accuracy can be misleading with imbalanced datasets where one class dominates. In such cases, consider precision, recall, or F1-score.

Q3: What's the difference between accuracy and precision?
A: Accuracy measures overall correctness, while precision measures the proportion of positive identifications that were actually correct.

Q4: Can accuracy be greater than 1?
A: No, accuracy ranges from 0 (worst) to 1 (best), representing the fraction of correct predictions.

Q5: How does accuracy relate to error rate?
A: Error rate is simply 1 minus accuracy, representing the fraction of incorrect predictions.