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Accuracy Equation:
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Accuracy measures how often the Quillbot AI Detector correctly identifies both AI-generated and human-written content. It's the ratio of correct predictions (true positives + true negatives) to total predictions made.
The calculator uses the accuracy equation:
Where:
Explanation: The equation calculates the proportion of correct classifications out of all classifications made by the detector.
Details: Accuracy is a fundamental metric for evaluating the performance of AI detection tools. Higher accuracy indicates better overall performance in distinguishing between AI-generated and human-written content.
Tips: Enter the number of true positives, true negatives, and total cases. All values must be non-negative integers, and the sum of TP and TN should not exceed the total cases.
Q1: What is considered a good accuracy score?
A: Generally, scores above 80% are considered good, but this depends on the specific application and baseline performance.
Q2: How does accuracy differ from precision and recall?
A: Accuracy measures overall correctness, while precision focuses on the correctness of positive predictions, and recall measures how many actual positives were identified.
Q3: Can accuracy be misleading?
A: Yes, in imbalanced datasets (e.g., mostly human content), high accuracy might not reflect good performance for the minority class (AI content).
Q4: How can I improve my AI detector's accuracy?
A: Use more diverse training data, regularly update your model with new examples, and consider ensemble methods.
Q5: Should I only rely on accuracy to evaluate performance?
A: No, it's best to consider multiple metrics (precision, recall, F1-score) along with accuracy for a complete picture.