1. What is Accuracy?

Accuracy is a metric that measures the proportion of correct predictions made by a model out of all predictions. It provides a simple way to evaluate the overall performance of classification models.

2. How Does the Calculator Work?

The calculator uses the accuracy formula:

Where:

• Correct Predictions — Number of correctly classified instances (unitless)
• Total Predictions — Total number of predictions made (unitless)

Explanation: The formula calculates the ratio of correct predictions to total predictions, typically expressed as a percentage by multiplying by 100.

3. Importance of Accuracy Calculation

Details: Accuracy is a fundamental evaluation metric in machine learning and statistics. It provides a quick overview of model performance, though it should be used in conjunction with other metrics like precision, recall, and F1-score for imbalanced datasets.

4. Using the Calculator

Tips: Enter the number of correct predictions and total predictions. Both values must be non-negative integers, and correct predictions cannot exceed total predictions.

5. Frequently Asked Questions (FAQ)

Q1: When is accuracy not a good metric?
A: Accuracy can be misleading for imbalanced datasets where one class significantly outnumbers others. In such cases, precision, recall, or F1-score may be more appropriate.

Q2: What is considered good accuracy?
A: Good accuracy depends on the context and problem domain. For binary classification, accuracy above 70-80% is generally considered good, but this varies by application.

Q3: Can accuracy be 100%?
A: Yes, if all predictions are correct, accuracy will be 100%. However, perfect accuracy is rare in real-world applications and may indicate overfitting or data leakage.

Q4: How does accuracy differ from precision?
A: Accuracy measures overall correctness, while precision measures the proportion of true positives among all positive predictions. They measure different aspects of model performance.

Q5: Should accuracy always be the primary metric?
A: No. While accuracy is important, other metrics like recall, precision, F1-score, or AUC-ROC may be more relevant depending on the specific business problem and cost of errors.