Sep. 22, 2021
Knowing how to assess the performance of a pattern recognition model is highly important for a wide variety of tasks in artificial intelligence, machine learning, and computer vision. Below, I’ll discuss some of the most widely used criteria for good pattern recognition systems.
Suppose that you’re working on a simple image classification task for pattern recognition: identifying when a photograph has a dog in it. You have 100 images of each type (dog vs. no dog) in your test dataset. The first step is to separate your model’s results into four types:
Once you have the figures for each of these four types, you can build a confusion matrix. Suppose your confusion matrix looks like this:
With this information, you can use the following metrics to assess your model’s performance:
In addition to precision, recall, and F1 score, another criterion used to evaluate machine learning and computer vision models is ROC-AUC. An ROC curve is used to visualize the performance of a binary classifier model, and AUC (“Area Under Curve”) is a metric for measuring how well the classifier can separate signal from noise. If AUC is equal to 1, the model can perfectly separate positive and negative classes; if it is equal to 0, the model incorrectly predicts all positives as negatives, and vice versa.