Overview
A ROC (Receiver Operating Characteristic) curve is a graph that illustrates how well an AI model distinguishes between two categories, such as mineral-rich versus barren drill targets. This curve helps evaluate the performance of the VRIFY.AI models.
By plotting the True Positive Rate (Sensitivity) against the False Positive Rate (1 - Specificity), the ROC curve helps you understand the model’s accuracy in predicting valuable exploration sites.
What the Curve Tells You
The ROC curve helps assess the model's balance between correctly identifying valuable targets (True Positives) and minimizing false predictions. A curve close to the top-left corner indicates strong performance with high accuracy and low false positives. A curve near the diagonal implies that the model is performing only slightly better than simply guessing, signalling the need to evaluate your parameters.
The area under the curve (AUC) provides a performance score, where 1.0 is perfect, and 0.5 reflects random guessing.
How to Interpret the Graph
The ROC curve plots the False Positive Rate on the x-axis and the True Positive Rate on the y-axis. Each point on the curve represents a different threshold used by the model to predict drill targets. Here’s how to interpret the key elements:
X-Axis (False Positive Rate): This measures the proportion of barren sites incorrectly classified as mineral-rich.
Y-Axis (True Positive Rate): This reflects the model’s success in correctly identifying known mineral-rich targets.
Simply put, the closer the curve is to the top-left corner, the better the model is at predicting valuable targets. If the curve is close to the diagonal, the model is performing poorly, suggesting that retraining, adjusting thresholds, or refining the model’s data features may be necessary for improvement.
Still have questions?
Reach out to your dedicated VRIFY AI Contact or email Support@VRIFY.com for more information.