Machine Learning Loss Functions

The basis for all machine learning models is an objective function which is to be either maximised or minimised based on type of problem.

The basis for all machine learning models is an objective function which is to be either maximised or minimised based on the type of problem.

This objective function is manifested as an error metric to reduce or improve upon. The gradient descent approach behind this optimisation attempts to reduce this error metric as an indication of a better fit of the model parameters to the data behaviour.

Given how the error metric influence the process of optimisation and the kind of fit we end up with, a basic understanding of the various metrics and attributes is warranted.

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