Error Matrix
A table used to assess the accuracy of classified data, especially in remote sensing and land cover analysis (standard GIS usage).
Explain the concept of Error Matrix?
An error matrix, sometimes referred to as a confusion matrix, is a table that compares expected and actual values to assess how well a classification model performs. It is frequently used to evaluate how well a model or system is classifying data in the domains of machine learning, remote sensing, and image classification. To enable a clear comparison of results, the matrix is usually organized with rows denoting the actual classes and columns denoting the predicted classes (or vice versa).
True positives (TP), where the model accurately predicts the positive class; true negatives (TN), where it accurately predicts the negative class; false positives (FP), where the model predicts the positive class incorrectly; and false negatives (FN), where it misses a positive case, are the primary elements of an error matrix. These numbers aid in the computation of crucial performance indicators that provide a more thorough understanding of the efficacy and dependability of the model, such as accuracy, precision, recall, and F1-score.
Related Keywords
In machine learning, a confusion matrix is a table that is used to assess how well a categorization model performs. To help determine how effectively the model predicts each class, it displays the counts of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN). It is crucial for assessing model efficacy beyond basic accuracy since it allows for the derivation of important metrics including accuracy, precision, recall, and F1-score.
The degree to which a classified remote sensing image resembles real-world features is measured by accuracy evaluation. Metrics like overall accuracy and Kappa coefficient are computed by comparing the image with reference (ground truth) data, guaranteeing accurate data for environmental study and mapping.
In order to evaluate classification accuracy, an error matrix, also known as a confusion matrix, contrasts predicted classes with actual ground truth. It displays accurate classifications on the diagonal and errors off-diagonal.
Reliability of geographical analysis is ensured by classification accuracy metrics in GIS, such as Overall Accuracy, Producer's Accuracy, User's Accuracy, and Kappa coefficient, which quantify how well classified maps match ground truth data.