Which concept is used to quantify the error between estimated values and actual labeled values?

The concept that is commonly used to quantify the error between estimated values and actual labeled values is the cost function. The cost function, also known as a loss function in some contexts, provides a quantitative measure of how well a model's predicted outcomes align with the true values. It does this by calculating the difference or "cost" of errors in the predictions made by the model.

In machine learning, a cost function guides the optimization process, helping to adjust the model parameters to minimize the difference between predicted and actual outcomes. For example, in regression tasks, common cost functions include Mean Squared Error (MSE) and Mean Absolute Error (MAE), which respectively measure the average squared and absolute differences between predicted values and true labels.

By minimizing the cost function, data scientists are able to improve their models' performance and achieve a better fit for the training data.

While "loss function" is also a correct term commonly used interchangeably with cost function, in the context of many optimization algorithms, the term "cost function" is often preferred, particularly when referring to a broader set of measures beyond individual predictions. Other terms like "error rate" and "accuracy measurement" focus more on performance evaluation rather than the quantification of the differences in