1. What is Accuracy?

Accuracy is a metric that measures the proportion of correct predictions (both true positives and true negatives) among the total number of cases examined. It's one of the simplest performance metrics for classification models.

2. How Accuracy is Calculated

The accuracy formula is:

Where:

• \( TP \) — True Positives (correct positive predictions)
• \( TN \) — True Negatives (correct negative predictions)
• \( FP \) — False Positives (incorrect positive predictions)
• \( FN \) — False Negatives (incorrect negative predictions)

Explanation: Accuracy ranges from 0 to 1, where 1 represents perfect classification and 0.5 represents performance equivalent to random guessing.

3. Importance of Accuracy

Details: While accuracy provides a quick overall measure of model performance, it can be misleading with imbalanced datasets where one class dominates. It's most useful when classes are roughly balanced.

4. Using the Calculator

Tips: Enter the counts from your confusion matrix. All values must be non-negative integers. The calculator will compute the accuracy score between 0 and 1.

5. Frequently Asked Questions (FAQ)

Q1: When is accuracy a good metric to use?
A: Accuracy is most appropriate when classes are balanced and when false positives and false negatives have similar consequences.

Q2: What are limitations of accuracy?
A: Accuracy can be misleading with imbalanced datasets. For example, a model that always predicts the majority class might have high accuracy but poor predictive power.

Q3: What alternatives exist to accuracy?
A: Precision, recall, F1 score, ROC-AUC, and other metrics may be more informative depending on your specific needs and data characteristics.

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

Q5: What does an accuracy of 0.5 mean?
A: An accuracy of 0.5 means the model performs no better than random chance for a binary classification problem.