When we build an AI model, the most important aspect of the process is to define a way to measure the performance of a model. This enables the data scientist to decide how to improve and pick the best model.
In this section, we will learn about three common metrics that are commonly used in the industry to assess the performance of the AI model.
Metric 1 – ROC curve
The Receiver Operating Characteristic (ROC) metric measures how well the classifier performs its classification job versus a randomized classifier. The classifier that's used in this metric is a binary classifier. The binary classifier classifies the given set of data into two groups on the basis of a predefined classification rule.
This is linked to a situation where, say, we compare this model against flipping a fair coin to classify the company as being default or non-default, with heads indicating default and tails indicating non-default....
