Average Error Across All Data, Cost Function
The cost function is a function that measures how much average error exists across the entire dataset when a machine learning model is learning.
It calculates the average of all data losses, thereby helping determine how well the model has been trained.
Similar to the loss function, the cost function uses various functions like Mean Squared Error, Mean Absolute Error, Cross-Entropy, etc.
Difference Between Loss Function and Cost Function
While the loss function and cost function are similar concepts, they differ in the unit of data they process.
The loss function refers to the error calculated for individual data sample.
For instance, when calculating the difference between the predicted value and the actual value for a single input data, the loss function is used.
On the other hand, the cost function represents the average error for the entire data samples.
In essence, the cost function is the average of the loss values from multiple data points.
Ultimately, what a machine learning model seeks to minimize in its learning process is the value of the cost function.
While the loss function is used to measure the error of individual samples, the cost function is employed to evaluate and optimize the model's overall performance.
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