loss contribution

In machine learning, the loss contribution of a particular example refers to the amount by which the loss function is affected by that example. The loss function is a measure of how well the model is fitting the data, and the loss contribution of each example is a way of quantifying the impact of that example on the overall performance of the model.

For example, in a classification problem, the loss function might be the cross-entropy loss, which measures the difference between the predicted probability distribution and the true distribution of labels. The loss contribution of each example would be the amount by which the cross-entropy loss is increased or decreased when that example is included in the training data.

By analyzing the loss contributions of different examples, we can gain insights into which examples are particularly important for the model's performance, and which examples may be less informative. This can help in selecting training data, identifying outliers, and improving the overall performance of the model.