In linear regression and logistic regression, without regularization, we can think the objective is to maximize likelihood.

On the other hand, we the term "loss function" is more general than likelihood.

For example, we can add regularization (See Regularization methods for logistic regression).

We can also add other constraints or use weighted loss. All of these are "add on" to likelihood.

In linear regression and logistic regression, without regularization, we can think the objective is to maximize likelihood.

On the other hand, we the term "loss function" is more general than likelihood.

For example, we can add regularization (See Regularization methods for logistic regression).

We can also add other constraints or use weighted loss. All of these are "add on" to likelihood.

Check this post for details

Objective function, cost function, loss function: are they the same thing?