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SHAP values explain a prediction by allocating the difference between a model’s baseline output and its output for one case across that case’s features. They can show how a model arrived at a prediction, but they do not prove that a feature caused the real-world outcome. The baseline, output scale, explainer and data used as a reference all affect what the values mean.
What SHAP values mean
SHAP stands for SHapley Additive exPlanations. It applies Shapley-value credit allocation from cooperative game theory to model predictions: each feature receives a contribution for a particular case. The SHAP project describes it as a game-theoretic approach to explaining a machine-learning model’s output; Lundberg and Lee’s 2017 paper framed it as a unified approach to assigning feature-importance values for individual predictions.
A useful accounting identity is:
model output = baseline expected output + sum of feature SHAP contributions
The baseline is the explainer’s expected output for its chosen reference or background data. Each feature’s SHAP value measures its allocated contribution relative to that baseline. A positive value moves the explained output upward; a negative value moves it downward. The contributions add up to the difference between the baseline and the case’s output when computed for the selected explainer and setup.
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“Upward” refers to the model output, not necessarily to a better or more likely real-world outcome. For a model predicting risk, for example, a positive contribution may raise predicted risk. Also check the units: an explanation may use a regression value, probability, raw model score or log-odds. A change of 0.2 in probability is not interchangeable with a change of 0.2 in log-odds.
What determines an explanation
SHAP values are conditional on how the explanation is constructed. They are not a single, setup-free measure of feature importance. Record these choices alongside an explanation so that another analyst can interpret or reproduce it.
- Prediction target: Identify the output being explained, such as a particular class’s probability or a regression prediction.
- Output scale or link: State whether contributions are in probability, raw-score, log-odds or another output space. TreeExplainer supports different output spaces, so the selected one matters.
- Background or masking data: This reference helps define what counts as an expected prediction and how missing or masked features are handled. Changing it can change both the baseline and feature attributions. DeepExplainer, for example, averages over background samples.
- Explainer and feature-dependence assumptions: Different methods and ways of handling feature relationships can allocate credit differently, especially when inputs are correlated.
This dependence is not a flaw unique to SHAP; it is part of what the explanation answers. A SHAP explanation describes a model relative to a specified reference and setup. It does not reveal an absolute, context-free effect of a feature.
Which SHAP explainer to use
Start with shap.Explainer when you want the library’s general interface, then choose or confirm an explainer suited to the model and task. The right choice balances compatibility, computational cost, approximation and output semantics.
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| Model or need | Possible explainer | What to know |
|---|---|---|
| Supported tree ensembles, including XGBoost, LightGBM, CatBoost, scikit-learn and PySpark integrations | TreeExplainer | The SHAP project documents a high-speed exact Tree SHAP algorithm for supported tree ensembles. Confirm the model integration and output setting you are using; exactness in this context does not remove dependence on the chosen explanation setup. |
| Linear models | LinearExplainer | Designed for linear models. As with other explainers, interpret values against the selected reference and account for feature relationships. |
| Differentiable deep-learning models | DeepExplainer | Extends DeepLIFT-style propagation with background samples to approximate SHAP values. Its stated complexity grows linearly with the number of background samples. |
| Models where broad model-agnostic compatibility matters | KernelExplainer or permutation/sampling-style explainers | These methods can work without a tree- or linear-specific algorithm, but estimate contributions and can be computationally expensive. Suitability depends on feature count, evaluation budget and the masking setup. |
AWS Prescriptive Guidance also recommends Tree SHAP and Kernel SHAP for local interpretation of individual predictions. That is applied guidance, not a guarantee that either method is appropriate for every model or question. When runtime or scale is a concern, test the explanation workflow on representative data before relying on it broadly.
How to build a defensible SHAP explanation
- Define the question. Specify which prediction is under review, which output or class matters, and whether you need a local explanation for one case or a summary across cases.
- Set the output scale. Identify whether the explainer returns contributions in the model’s raw output, probability or another supported space. Keep the baseline, contributions and final prediction on the same scale.
- Choose a meaningful reference. Select background or masking data that represents the comparison you want the explanation to make. A different reference can change the expected output and redistribute feature contributions.
- Match the explainer to the model. Consider TreeExplainer for supported tree ensembles, LinearExplainer for linear models, and DeepExplainer or another neural-network method for differentiable deep models. Use model-agnostic methods such as Kernel or permutation approaches when compatibility is more important than computation time.
- Check a local explanation first. For selected cases, verify that the baseline plus the feature contributions reaches the prediction on the chosen output scale. Investigate surprising attributions against the input row and model behavior.
- Test sensitivity before drawing conclusions. Re-run explanations with reasonable alternative background samples, data slices and model versions. Examine correlated inputs and possible interactions; do not treat a ranking as stable just because it appears in one run.
- Use domain validation. Treat the result as evidence about model behavior, then check important conclusions with domain knowledge and, where the question is causal, appropriate causal methods.
How to read common SHAP plots
Plots summarize the values; they do not change their meaning. Check the output units and numeric axis before interpreting colors, position or ranking.
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| Plot | What it shows | How to read it |
|---|---|---|
| Waterfall | How feature contributions for one row move the baseline to that row’s prediction | Start at the baseline and follow the signed contributions to the final output. The bars show the accounting for that case, not a causal sequence. |
| Beeswarm | The distribution of feature SHAP values across the analyzed rows, commonly ordered by average absolute contribution | Each point represents a row’s contribution for a feature. Horizontal position indicates the SHAP value and therefore direction and magnitude on the selected output scale. Color conventionally represents feature value, but colors and ordering are plot conventions; read the legend and axes. |
| Bar summary | A global ranking, often based on mean absolute SHAP value across rows | It summarizes average contribution magnitude, not whether a feature usually raises or lowers predictions. It also does not measure a feature’s real-world importance or causal effect. |
| Dependence plot | How a feature’s observed values relate to its SHAP values across cases | Look for variation, direction changes and patterns that may signal interactions. Correlation with another input can complicate attribution, so the plot alone does not establish which variable drives the pattern. |
Waterfall and force visualizations commonly use red and blue to distinguish upward and downward contributions. Those colors are visualization choices, not universal semantics; use the plot’s labels and numeric values.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What SHAP cannot establish
A large positive SHAP value means that, under the selected model and explanation setup, a feature was allocated credit for moving the output upward from the baseline. It does not show that changing that feature in the real world would cause the outcome to change. The model may use a feature as a proxy, rely on an association that will not hold under intervention, or reflect patterns in training data that do not generalize.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsCorrelated features are a particular caution: they can share or redistribute credit, and an attribution can depend on how the explainer treats feature dependence. Different explainers, output links, masks and background data may produce different allocations. Mean absolute SHAP values can help rank a model’s average contribution magnitudes on an analyzed dataset, but the resulting ranking is a description of that model and dataset—not a causal or universal ranking of real-world drivers.
Use SHAP for tasks such as reviewing individual predictions, debugging model behavior, investigating potential fairness concerns and monitoring changes in patterns. For consequential claims, pair it with sensitivity checks, domain expertise and methods designed to answer causal questions.
Example: reading the size of a dataset claim correctly
The SHAP project tutorial introduces regression explanations with the California housing dataset, described as 20,640 blocks of houses and 8 input features from 1990 data. Those figures describe that tutorial dataset; they are not a recommended minimum dataset size, a benchmark of SHAP performance or evidence that an explanation will generalize to another model or population.
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