Test loss: Difference between revisions

Machine learning algorithms measure their model's ability to make accurate predictions on unseen data. The test loss provides an assessment of a model's generalization ability, or its capacity for making accurate predictions when presented with new, unseen information that was not seen during training.

The test loss is calculated by comparing the model's predictions on test data with actual values for target variables. This difference, known as an "error" or "residual", serves to measure how accurately predictions made on the test data reflect actual outcomes. It serves to reflect how well-fitted the model's predictions were to the actual data.

Calculating a test loss requires consideration of the particular problem being addressed and desired properties of the model. Common loss functions include mean squared error, mean absolute error, and categorical cross-entropy.

Mean squared error (MSE) is a commonly used measure when attempting to predict an ongoing target variable. MSE is calculated as the average of squares between predicted values and actual values, or MSE for short.

MSE is a smooth and differentiable function, making it suitable for optimization algorithms such as gradient descent. Furthermore, MSE has the advantageous property of being sensitive to large errors; this means a model with an increased MSE is likely to make major mistakes on some instances in its test set.