Accuracy Calculator
Calculate statistical accuracy and error rates from confusion matrix components
Statistical accuracy is a measure of how well a test, model, or measurement system correctly identifies or predicts outcomes. It is expressed as the proportion of true results (both true positives and true negatives) among the total number of cases examined.
In statistical analysis and machine learning, accuracy is a crucial metric for evaluating the performance of classification models, diagnostic tests, and other binary decision systems.
The formula for calculating accuracy is:
Accuracy = (True Positives + True Negatives) / Total Cases × 100%
Error Rate = (False Positives + False Negatives) / Total Cases × 100%
Where Total Cases is the sum of:
- True Positives (TP)
- True Negatives (TN)
- False Positives (FP)
- False Negatives (FN)
True Positives (TP)
Cases that were correctly identified as positive. For example, correctly diagnosing a patient with a disease they actually have.
True Negatives (TN)
Cases that were correctly identified as negative. For example, correctly identifying a healthy patient as not having a disease.
False Positives (FP) - Type I Error
Cases that were incorrectly identified as positive. For example, diagnosing a healthy patient with a disease they don't have.
False Negatives (FN) - Type II Error
Cases that were incorrectly identified as negative. For example, failing to diagnose a patient with a disease they actually have.
Accuracy calculations are widely used in various fields:
- Medical Diagnostics: Evaluating the performance of diagnostic tests and screening procedures
- Machine Learning: Assessing classification model performance
- Quality Control: Measuring the accuracy of manufacturing processes and inspection systems
- Scientific Research: Validating experimental results and measurement systems
- Information Retrieval: Evaluating search engine and recommendation system performance
What is a good accuracy score?
What constitutes a “good” accuracy score depends on the specific context and application. In some fields, 90% might be considered excellent, while in others, 99.9% accuracy might be required. It's important to consider the consequences of errors and industry standards.
Why is accuracy alone not always sufficient?
Accuracy alone may not be sufficient when dealing with imbalanced datasets or when the costs of different types of errors vary significantly. In such cases, other metrics like precision, recall, or F1 score might be more appropriate.
How can I improve accuracy?
Improving accuracy often involves collecting better data, using more sophisticated measurement or classification methods, implementing better quality control procedures, or refining the testing process. The specific approach depends on the application context.