We’ll need to evaluate the performance of the detector built to ensure that we are achieving a higher true positive rate than a false positive rate. Also as we increase the types of features built and used, we’ll need to monitor their performance. ROC Curve In order to evaluate the performance of the detector, we are going to use the Receiver Operating Characteristic (ROC) curve. We plot the false-positive rates against the true positive rates at various thresholds. This will help determine how to configure our detector to get the optimal settings. Detectors are not perfect, there will be false positives but we can use this method to reduce the false positive rate and increase our true positive rate. When you think about the process and the possibilities then it seems like a never-ending story but we should look at it as evolving our detector. As we implement our function to evaluate the detector performance, we will delve further into the requirements of the ROC curve and ...