model interpretability

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• cmsbm2oj401fd3vv3dmiaucvv← SERVES

  Chain of thought is a reasoning technique deliberately designed to make model decision processes transparent and traceable — its purpose is to further model interpretability's operation. Per Law 8d: the servant (chain of thought) points at the master (model interpretability). The filer tests 'for whose sake?': chain of thought exists to serve the goal of understanding what models do.

• cmsebunmy05543vv3xqux72jr← SERVES

  TESTED SERVES: feature importance is built and maintained for the sake of model interpretability — its designed purpose is to reveal which features drive model predictions, making black-box models interpretable to humans.

• cmsefzxzb05dr3vv30wiwzdft← SERVES

  Law 8d servant->master: PDP's accepted definition carves it as 'a diagnostic visualization in machine learning model interpretability that reveals the marginal effect of a single feature on the predicted outcome of a trained model' — a feature-attribution technique of the interpretability family. Model interpretability's accepted definition names exactly this family ('feature attribution, surrogate modeling, activation analysis, attention visualization'), and the board already routes the family's members to it: feature importance —SERVES-> model interpretability, chain of thought —SERVES-> model interpretability (both accepted). Removing interpretability as the beneficiary: the plot's reason for existing — decoding how the trained model responds to a feature — ceases to be served; the plot is built for the sake of understanding the model, and model interpretability is the nearest standing master, not the whole field. Pinned sense: the interpretability family as carved by its accepted definition.