Explaining loss reduction

Prediction attribution explains a model output. Loss attribution instead explains how features reduce loss relative to the baseline prediction, using the observed outcome for each row. These methods are available on CPU TreeIG.

Regression

Given a fitted regression model, evaluation rows X_eval, outcomes y_eval, and a baseline x0:

from treeig import TreeIG

ig = TreeIG(model, baseline=x0)
loss = ig.loss_attribution(X_eval, y_eval, loss="squared_error")
print(loss["values"])
print(loss["total"])

observation_values has one row of feature contributions per observation. values averages those rows; standard_errors describes uncertainty of that mean across the supplied observations. It does not include model-fitting or baseline-selection uncertainty. total is baseline_loss - model_loss. Positive contributions reduce loss; negative contributions increase it.

Classification

Use loss="log_loss" for a binary margin model, with labels encoded as 0 and 1 and the positive-class margin (target=None or target=1). Do not substitute probabilities for margins.

ig = TreeIG(binary_model, baseline=x0, target=1)
loss = ig.loss_attribution(X_eval, y_binary, loss="log_loss")

For multiclass models, use the dedicated method so that changes across all class margins are combined before the softmax loss is evaluated:

ig = TreeIG(multiclass_model, baseline=x0, target=0)
loss = ig.multiclass_loss_attribution(X_eval, y_class_indices)

Labels are class indices, not arbitrary class names. Supply n_classes for a model without classes_. The returned aggregate fields follow the same loss-reduction convention as regression.

Baseline distributions

Both methods accept baseline rows and weights, including a constructor default. They average the loss reductions from the individual baseline paths. This is not generally the loss reduction from the mean baseline prediction. Loss attribution operates on the sequence of prediction changes; applying a loss function to already aggregated prediction attributions is not equivalent.