Machine learning notes: Concept comparison - loss function, cost function, objective function

Mobile 2023-09-30 21:02:48 views: null

Loss FunctionLoss Function

Usually for a single training sample

Given a model output $\hat{y}$ and a true value y, the loss function is $L=f(\hat{y},y)$

Cost FunctionCost Function

Usually the total loss over the entire training set (or a mini-batch when using mini-batch gradient descent)

$J=\sum_{i=1}^N f(y_i,\hat{y_i})$

Objective Function

Represents any function that wishes to be optimized

The cost function is a type of objective function

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