1. What is Accuracy Ratio?

Accuracy ratio measures the proportion of correct predictions (both true positives and true negatives) among the total cases examined. It's a fundamental metric in evaluating classification models.

2. How Does the Calculator Work?

The calculator uses the Accuracy formula:

Where:

• \( TP \) — True Positives (correctly identified positive cases)
• \( TN \) — True Negatives (correctly identified negative cases)
• \( Total \) — Total number of cases examined

Explanation: The equation calculates the ratio of correct predictions to total predictions, providing a simple measure of overall model performance.

3. Importance of Accuracy Calculation

Details: Accuracy is crucial for evaluating classification models, comparing different algorithms, and understanding overall model performance in machine learning and statistics.

4. Using the Calculator

Tips: Enter the number of true positives, true negatives, and total cases. All values must be non-negative integers with total > 0.

5. Frequently Asked Questions (FAQ)

Q1: What is a good accuracy score?
A: Generally, >70% is acceptable, >80% is good, and >90% is excellent, but this depends on the application and baseline rates.

Q2: When is accuracy not a good metric?
A: In imbalanced datasets where one class dominates, as a model can achieve high accuracy by simply predicting the majority class.

Q3: How does accuracy differ from precision?
A: Accuracy measures overall correctness, while precision focuses on the proportion of positive identifications that were actually correct.

Q4: Can accuracy be greater than 1?
A: No, accuracy is always between 0 (worst) and 1 (best), often expressed as a percentage (0-100%).

Q5: What's the relationship between accuracy and error rate?
A: Error rate = 1 - Accuracy, representing the proportion of incorrect predictions.