To learn, an ML model needs an objective function to guide the learning process.12 An objective function is also called a loss function, because the objective of the learning process is usually to minimize (or optimize) the loss caused by wrong predictions. For supervised ML, this loss can be computed by comparing the model’s outputs with the ground truth labels using a measurement like root mean squared error (RMSE) or cross entropy.
