user's accuracy and producer's accuracy compute from true positive, true negative, false negative and false positive
In the context of classification problems, particularly in remote sensing and other fields where confusion matrices are used, the terms "user's accuracy" and "producer's accuracy" are often used to evaluate the performance of a classification model. These metrics can be computed using the values from a confusion matrix: true positive (TP), true negative (TN), false positive (FP), and false negative (FN).
User's accuracy, also known as precision, measures the accuracy from the perspective of the user of the classification map. It indicates the probability that a pixel classified into a given class actually represents that class on the ground.
For a specific class, user's accuracy is calculated as: [ \text{User's Accuracy} = \frac{\text{True Positive (TP)}}{\text{True Positive (TP)} + \text{False Positive (FP)}} ]
Producer's accuracy, also known as recall or sensitivity, measures the accuracy from the perspective of the producer of the classification map. It indicates the probability that a pixel on the ground is correctly classified.
For a specific class, producer's accuracy is calculated as: [ \text{Producer's Accuracy} = \frac{\text{True Positive (TP)}}{\text{True Positive (TP)} + \text{False Negative (FN)}} ]
Let's consider a binary classification problem with the following confusion matrix:
| Predicted Positive | Predicted Negative | |
|---|---|---|
| Actual Positive | TP = 50 | FN = 10 |
| Actual Negative | FP = 5 | TN = 100 |
For the positive class:
User's Accuracy (Precision): [ \text{User's Accuracy} = \frac{TP}{TP + FP} = \frac{50}{50 + 5} = \frac{50}{55} \approx 0.909 ]
Producer's Accuracy (Recall): [ \text{Producer's Accuracy} = \frac{TP}{TP + FN} = \frac{50}{50 + 10} = \frac{50}{60} \approx 0.833 ]
These metrics provide insights into the performance of the classification model from different perspectives, helping to understand both the reliability of the classification results (user's accuracy) and the model's ability to identify all relevant instances (producer's accuracy).