Abacus.AI - Evaluating Predictions

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Problem type: Classification

As the name suggests, the classification model takes the input and classifies it as a class label. For example, a binary classifier trained on a set of dog images would classify an image as dog or not dog. With that, let me introduce a quick evaluation recipe for those who might have a company to run and who just want to get a high-level idea of the quality of the model in a few seconds:

Classification

Quick Evaluation Recipe

Compare metric scores with the baseline scores to see if there's an improvement or not
Start with AUC, make sure it's between 0.6 and 0.95 (higher the AUC the better)
Check accuracy, anything above 60% means something useful is being learned
Check precision and recall, one of them should be more than 0.5 for useful results
Finally, go to the prediction dashboard and see if the results generated by the model matched with actual class labels for different classes

If all of the above things look good then the model is good to go and you have trained a world-class deep learning model in just a few minutes with only a few clicks.