Papers › A Unified Approach to Interpreting Model Predictions
A Unified Approach to Interpreting Model Predictions
Scott Lundberg, Su-In Lee
Understanding why a model makes a certain prediction can be as crucial as the prediction's accuracy in many applications. However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between accuracy and interpretability. In response, various methods have recently been proposed to help users interpret the predictions of complex models, but it is often unclear how these methods are related and when one method is preferable over another. To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is a unique solution in this class with a set of desirable properties. The new class unifies six existing methods, notable because several recent methods in the class lack the proposed desirable properties. Based on insights from this unification, we present new methods that show improved computational performance and/or better consistency with human intuition than previous approaches.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="1705.07874")
Code
Syntology Ran 3 of 8 code samples harvested from 3 repositories linked to this paper; 5 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · violated contract; 1 ran with no contract checked.
By repository: community (archive-listed): 6 samples from 3 repositories, 1 ran; 2 identical to code first harvested elsewhere. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
17 repositories listed; official and paper-mentioned ones first.
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
8 samples harvested; 3 ran; 1 honoured the contract we drafted; 5 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 6 of the 8 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from 3 repositories linked to this paper, official or community; each sample names its own and says which. Some samples are identical code Syntology first harvested from another repository; for those, this paper's copy is not located and its licence is not recorded. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.
Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.
8a5c823af20228ab · report
4ad3529c6a0d35d4 · report
d229caa00dd35ea5 · report
1bae926aa4169f77 · report
324da4ba58dcc892 · report
a0ec30609289d2f1 · report
3514bf75f7ea90b7 · report
ddb5cac385e0f2fb · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Attribution | CUB-200-2011 | Kernel SHAP | Deletion AUC score (ResNet-101) | 0.1016 | #6 of 8 | Archive leaderboard | report |
| Image Attribution | CUB-200-2011 | Kernel SHAP | Insertion AUC score (ResNet-101) | 0.6763 | #6 of 8 | Archive leaderboard | report |
| Image Attribution | CelebA | Kernel SHAP | Deletion AUC score (ArcFace ResNet-101) | 0.1409 | #4 of 8 | Archive leaderboard | report |
| Image Attribution | CelebA | Kernel SHAP | Insertion AUC score (ArcFace ResNet-101) | 0.5246 | #4 of 8 | Archive leaderboard | report |
| Image Attribution | VGGFace2 | Kernel SHAP | Deletion AUC score (ArcFace ResNet-101) | 0.2034 | #5 of 8 | Archive leaderboard | report |
| Image Attribution | VGGFace2 | Kernel SHAP | Insertion AUC score (ArcFace ResNet-101) | 0.6132 | #5 of 8 | Archive leaderboard | report |
| Interpretability Techniques for Deep Learning | CelebA | Kernel SHAP | Insertion AUC score | 0.5246 | #3 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections