SHAP is a family of methods for attributing a model’s output to its input features; it is not a single universal explainer. The SHAP project documents a Python package and API. ML.NET, meanwhile, provides model-specific feature contribution calculation for supported prediction transformers, but its reviewed documentation does not establish that this API computes SHAP values. For a .NET application, choose between running SHAP in a Python service, using ML.NET’s contribution API where supported, or keeping inference in .NET and handling explanation as a separate system.
What SHAP explains—and what it does not
SHAP stands for SHapley Additive exPlanations. The SHAP project documentation describes it as “a game theoretic approach to explain the output of any machine learning model.” In practice, SHAP assigns feature attributions relative to an explanation setup, so a reader can see how features contribute to a particular output under the selected method and assumptions.
An attribution is not evidence that a feature caused the outcome. It describes model behavior, not the real-world causal effect of changing a feature. Interpret every result in light of the model, the value being explained, the feature representation, the masker or background data, and the output or class selected.
Which SHAP explainer should you use?
SHAP offers multiple explainer families, not one algorithm that fits every model. Its API reference documents specialized methods as well as model-agnostic choices. Compatibility is only one decision: the explanation target, masker or background, feature representation, and whether you need an individual prediction or broader patterns across samples also matter.
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| Explainer family | Typical fit | Selection consideration |
|---|---|---|
TreeExplainer |
Ensemble tree models | Use for compatible tree models; confirm the model and output setup are supported by the API. |
LinearExplainer |
Linear models | Explanation depends on the feature and background setup; do not assume its results are interchangeable with a tree or permutation method. |
DeepExplainer |
Deep-learning models | Choose it for a compatible deep model and define the explanation setup appropriate to that model. |
PermutationExplainer |
Model-agnostic use cases | Interpret the result as the output of a permutation-based explanation method, not as a universal measure of importance. |
PartitionExplainer |
Model-agnostic use cases | Consider how features are grouped or represented in the explanation setup. |
SamplingExplainer |
Model-agnostic use cases | Its sampling-based approach is a distinct method; the cited API does not provide a comparative speed or accuracy ranking. |
KernelExplainer |
Model-agnostic use cases | Check that the model function, masker or background, and output of interest are defined appropriately. |
The SHAP API also exposes a common Explainer interface that accepts a model or function and a masker, and can select or receive an algorithm. Newer API results are represented by an Explanation object. Consult the API for the chosen version rather than assuming all explainers accept identical inputs or produce interchangeable interpretations.
SHAP versus other XAI measures
“Explainable AI” covers methods with different goals. A local feature attribution describes how a method allocates a particular model output among features for an instance. A global summary aggregates evidence across examples to reveal broader model patterns. Permutation importance, feature contribution scores, and SHAP attributions are not interchangeable simply because all mention features.
- Start with the question: Are you explaining one prediction, comparing many predictions, or assessing an overall model pattern?
- Identify the output: Specify the class, score, or other prediction quantity to explain.
- Record the setup: Preserve feature names and representation, as well as the masker or background data and explainer choice.
- Keep the claim narrow: An attribution supports a statement about the model’s behavior under that setup, not a causal conclusion about the world.
Does ML.NET support SHAP?
ML.NET documents CalculateFeatureContribution for supported prediction transformers. It calculates model-specific feature contribution scores and exposes options for the number of positive and negative contributions and whether to normalize them. That is a documented feature-contribution API; the reviewed Microsoft documentation does not establish that it computes SHAP values.
Microsoft’s API reference identifies the API in an ML.NET v4.0.1 preview context. Check the package and API version used by your project before adapting examples or signatures.
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How to interpret the linear-model example
In the Microsoft Learn API example, Microsoft states that “for the linear model, the feature contributions for a feature in an example is the feature-weight*feature-value.” It also says, “The total prediction is thus the bias plus the feature contributions.” This describes the example’s linear-model contribution calculation; it is not a claim that every supported transformer uses that formula or that the API implements SHAP semantics.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Practical .NET integration choices
Option 1: Run SHAP in Python and call it from .NET
The SHAP project documents installation as a Python package and provides a Python API. A practical architecture is to run explanation computation in a Python service or job and have the .NET application request and present the result over an application-defined interface. This is an architectural recommendation, not a vendor-documented SHAP-to-ML.NET bridge or a tested integration supplied by the SHAP documentation.
Design the boundary so results remain interpretable: include feature names, the output or class identity, the prediction value being explained, and the explainer and background or masker context. Keep the model inputs and feature representation aligned between prediction and explanation; otherwise a correctly transported attribution may describe a different input than the one the application showed.
Option 2: Use ML.NET feature contributions where supported
If the model is a supported prediction transformer and model-specific scores meet the product’s needs, use CalculateFeatureContribution and describe the result accurately as ML.NET feature contributions. Check the API version, transformer support, output dimensions, and normalization behavior in the project’s actual package. Do not label the output “SHAP values” unless the concrete implementation’s documentation establishes that meaning.
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Option 3: Run ONNX or TensorFlow inference in .NET, with explanations handled separately
Microsoft documents consuming ONNX and TensorFlow models for inference in .NET applications; its ML.NET documentation for ONNX and TensorFlow inference describes that route. ONNX Runtime supports ONNX inference. This can keep prediction inside a .NET application, but it does not itself establish that the ONNX path calculates SHAP values. Decide separately where and how explanations will be computed.
Quick Recap
A decision checklist before shipping explanations
- Choose a method compatible with the model and the question you need to answer.
- Define the explained output, feature representation, and masker or background data.
- Distinguish an individual-prediction explanation from an aggregate view across samples.
- Verify that explanations correspond to the same input and model version as the displayed prediction.
- Keep SHAP attributions, ML.NET contributions, and other importance measures labeled by their actual method.
- Present explanations as accounts of model behavior, not proof of causation.
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