Explain and audit machine-learning predictions with SHAP. Use for selecting SHAP explainers and maskers, computing and validating feature attributions, handling multi-output explanations, and producing local or global SHAP visualizations.

SHAP Use SHAP to describe how a fitted predictive model maps inputs to outputs. Work from the modern shap.Explanation API, make the explained output and background distribution explicit, and validate every explanation before interpreting it. This skill is aligned with SHAP 0.52.0 (released 2026 05 28). That release requires Python 3.12 or newer. The maintained examples were checked on Python 3.12.10 with SHAP 0.52.0, NumPy 2.5.3, pandas 3.0.6, scikit learn 1.9.1, and matplotlib 3.11.2. Native tests cover small tree, exact, permutation, partition, linear, additive, kernel, text, and constant image games; additional numeric XGBoost 3.4.1 smoke checks cover raw, probability, loss, and interaction outputs. Reference snippets that require a project model, framework, or data are adaptation templates; optional pretrained/deep, GPU, and distributed integrations remain illustrative and require their own runtime validation. Operating Rules 1. Explain a fixed, evaluated model; do not use SHAP as a substitute for predictive validation. 2. Use held out or clearly labeled analysis rows for explanations. Choose background rows only from an appropriate training or reference population. 3. State the explained output: regression value, raw margin, probability, log loss, logit, or another model method. 4. Prefer shap.Explanation objects and explainer(X) . Some specialized options still require .shap values(X) , including deep ranked outputs, gradient sampling budgets, and Kernel SHAP nsamples . Preserve their output indexes and baselines explicitly. 5. For multi output models, select one output before using tabular plots: explanation[..., output index] . 6. Check base values + values.sum(...) against the exact model output being explained. 7. Treat SHAP as a description of model behavior under a masking/background choice. It does not establish causality, fairness, recourse, or scientific mechanism. 8. Never silence an additivity failure until input shape, preprocessing, model version, output space, and row ordering have been checked. 9. Do not load untrusted pickle, joblib, model, or explainer artifacts; those formats can execute code during deserialization. Install Create an isolated environment and pin the documented release: shap[plots] installs the plotting dependencies. Add the fitted model's package at a version compatible with the project. For older Python compatibility, read [references/migration.md](references/migration.md) instead of silently installing a different SHAP release. Confirm the environment before debugging an API mismatch: Standard Workflow 1. Define the explanation target Record: model and preprocessing version; exact callable or model method being explained; output name/index and units; evaluation rows; background/reference population; masker and explainer algorithm; SHAP and model library versions. For classifiers, decide whether the task needs raw margins or probabilities. Defaults differ by model family; never infer units from the plot color or sign. 2. Select an explainer and masker Start with shap.Explainer(model, masker) when automatic dispatch is sufficient. Instantiate a specialized explainer when its assumptions or output controls matter. Situation Preferred choice Important constraint Supported tree ensemble TreeExplainer model output="probability" and "log loss" require interventional masking and background data Linear model LinearExplainer The masker determines interventional versus correlation aware behavior Small feature space ExactExplainer Cost grows quickly with unconstrained feature count General tabular callable PermutationExplainer Budget at least one full forward/reverse permutation Hierarchical feature groups, text, or image PartitionExplainer The partition tree changes the cooperative game Differentiable neural network DeepExplainer or GradientExplainer Framework support, output shape, and background choice require testing Legacy Kernel SHAP workflow KernelExplainer Usually much slower than model specific methods Use the detailed decision guide in [references/explainers.md](references/explainers.md). Use [references/data maskers.md](references/data maskers.md) when features are correlated, structured, sparse, or semantically grouped. 3. Compute a modern Explanation This complete binary classification example uses an explicit background and selects output index 1. In the breast cancer dataset, class 1 means benign , so positive SHAP values below increase predicted benign probability, not cancer risk. For another dataset, resolve the requested label through model.classes and record its meaning before selecting an output index; column 1 is not universally the clinically positive event. Output shape is model dependent: one tabular output: (samples, features) ; multiple tabular outputs: (samples, features, outputs) ; multiple model inputs: often a list of arrays or explanations; image/text explanations: feature axes follow the input representation, with output selection on the final axis when present. Do not use the pre 0.45 pattern values[class index] for a modern multi output array. Use values[..., class index] or slice the Explanation itself. 4. Control tree output semantics when needed For a supported tree classifier, probability space explanations must be explicit: A bare background frame is capped at 100 rows by the default masker. The explicit masker above retains all 200 sampled rows; inspect len(explainer.data) when reporting or comparing background sizes. In SHAP 0.52: feature perturbation="auto" uses interventional semantics when background data is supplied and tree path dependent semantics otherwise; probability and log loss output modes are supported only with interventional semantics; pass approximate=True to explainer(X, approximate=True) if deliberately using the lower fidelity tree approximation; do not pass it to the constructor. 5. Use a model agnostic callable deliberately Pass the exact callable whose outputs will be interpreted: Increase max evals to average over more permutations when estimates are unstable. Keep the seed, background sample, and evaluation budget in the report. 6. Visualize the question, not merely the available plot Question Plot Which features have the largest average attribution magnitude? shap.plots.bar(exp) How do direction, magnitude, and observed values vary globally? shap.plots.beeswarm(exp) Why did one prediction differ from its baseline? shap.plots.waterfall(exp[i]) How does one feature's attribution vary over its values? shap.plots.scatter(exp[:, feature]) Do explanations form sample level patterns? shap.plots.heatmap(exp) How do predefined cohorts differ descriptively? shap.plots.bar(exp.cohorts(labels).abs.mean(0)) Which tokens or image regions contribute to an output? shap.plots.text(exp) or shap.plots.image(exp) Read [references/plots.md](references/plots.md) before customizing or saving figures. 7. Report limitations with results At minimum, report: output and units; baseline/reference population; explainer and masker; sample count and selection; output index/name; additivity error or applicable approximation diagnostics; known correlated/grouped features; whether results are local, aggregated, or cohort specific; a clear non causal statement. Common Tasks Global and local analysis Use global plots to locate important patterns, scatter plots to inspect those patterns, and local plots to investigate selected rows. Do not select only visually dramatic rows without documenting the selection rule. Multiclass models Set output names where possible, inspect explanation.output names , and slice an output before plotting: In 0.52.0, the generic permutation selector may drop supplied output names, and combining an ellipsis with a string output index can fail. Verify the class mapping, set names explicitly when needed, and resolve names to integer indexes before slicing. Never average signed attributions across classes. For cross class comparison, preserve the same model, rows, background, output space, and aggregation. Cohorts, subgroup analysis, and fairness SHAP can compare how a model uses features across cohorts, but this is not a fairness test. A protected feature with small SHAP magnitude does not rule out proxy discrimination, and removing a protected feature does not establish fairness. Pair attribution analysis with performance, calibration, error rate, and domain appropriate fairness metrics. See [references/workflows.md](references/workflows.md) for cohort construction, model comparison, error analysis, log loss explanations, monitoring, and production records. Text and images Use domain maskers rather than treating tokens or pixels as ordinary independent columns: shap.maskers.Text(tokenizer) with PartitionExplainer for token groups; shap.maskers.Image(...) with PartitionExplainer for image regions; restrict expensive multi output models with outputs=... . Read [references/modalities.md](references/modalities.md) for current examples and output shape guidance. Troubleshooting Order 1. Print Python, SHAP, model library, NumPy, and framework versions. 2. Verify the model receives exactly the same transformed columns, order, dtype, and missing value representation used during fitting. 3. Print values.shape , base values.shape , data.shape , feature names , and output names . 4. Confirm the selected output and output units. 5. Recompute predictions on the same rows in the same order. 6. Test a smaller batch and representative background. 7. Only then investigate package specific compatibility or approximation settings. Use [references/troubleshooting.md](references/troubleshooting.md) for additivity failures, shape mismatches, categorical features, pipelines, deep learning frameworks, plotting, and performance. Bundled Script Run a deterministic, self contained tabular example that writes importance data, metadata, and plots: The script labels the default output as benign probability , retains the requested background up to the training set size, and rejects non finite validation tolerances. Its synthetic software checks and built in dataset demo do not validate causal or clinical claims. Some SHAP 0.52.0 forest configurations fail explicit reconstruction (including seed 3 with 150 background rows); the script rejects those without writing report artifacts. See [references/troubleshooting.md](references/troubleshooting.md). It exports feature importance.csv , first row contributions.csv , prediction reconstruction.csv , and metadata.json , plus bar.png , beeswarm.png , waterfall first row.png , and scatter top feature.png . Plot titles identify the selected class probability. The script does not download data or deserialize models. Read it as a template, then replace the built in dataset and model while preserving output selection and additivity validation. Reference Map File Load when [references/explainers.md](references/explainers.md) Selecting or configuring explainers [references/data maskers.md](references/data maskers.md) Choosing background data, masking semantics, or feature groups [references/plots.md](references/plots.md) Selecting, composing, or saving visualizations [references/workflows.md](references/workflows.md) Running audits, comparisons, cohorts, monitoring, or production workflows [references/modalities.md](references/modalities.md) Explaining text, images, or deep models [references/migration.md](references/migration.md) Updating legacy SHAP code or supporting older Python [references/theory.md](references/theory.md) Explaining estimands, gu