What is the difference between loss and cost function?

[codeperfectplus]

What is the difference between loss and cost function?

[shahriar-rahman]

The loss function refers to the difference between the actual and predicted values for a single training example. In contrast, the Cost function refers to aggregating the differences over the entire dataset and is often used for model optimization.

[AITopDEV]

In other words, the loss function is to capture the difference between the actual and predicted values for a single record whereas cost functions aggregate the difference for the entire training dataset. The Most commonly used loss functions are Mean-squared error and Hinge loss.

[AtharvaDomale]

Loss function measures the error for one data point.
Cost function sums or averages the errors for all data points.

[Goutamxd]

@codeperfectplus ,The loss function calculates the error between the predicted and actual values for a single training example. On the other hand, the cost function aggregates this loss across the entire dataset, helping in model optimization.

Simply put: Loss function = Individual error, Cost function = Overall error across dataset. Common loss functions include MSE, Cross-Entropy, and Hinge Loss. 🚀