Loss and Metric

In machine learning, we train models to make predictions based on data. To assess their performance, we need a way to measure how well the models are doing. That's where loss and metrics come into the picture. Loss is a measurement of how incorrect the model's predictions are compared to the actual answers, and the goal is to minimize it. Metrics, on the other hand, give us an overall measure of the model's performance. By optimizing both loss and metrics, we can train models that make accurate predictions and decisions in artificial intelligence and machine learning.

4 Ways to Learn