Every machine learning engineer should know about these common loss functions and when to use them.
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1. Introduction to the loss function
A loss function is a way to evaluate how well a machine learning algorithm models a dataset of features. In other words, the loss function is a measure of how good the model is at predicting the expected outcome.
Cost function and loss function refer to the same context (i.e. the training process using backpropagation to minimize the error between the actual and predicted results). We compute the cost function as the average of all loss function values, and we compute the loss function for each sample output compared to its actual value.
The loss function is directly related to the predictions of the model you build. If your loss function value is low, your model will give good results. The loss function (or rather, the cost function) that you use to evaluate the performance of your model needs to be minimized to improve its performance.
What is the loss function in machine learning?
A loss function is a way to evaluate a machine learning algorithm on a feature dataset