shap

SHAP (SHapley Additive exPlanations)

Overview

SHAP is a unified approach to explain machine learning model outputs using Shapley values from cooperative game theory. This skill provides comprehensive guidance for:

Computing SHAP values for any model type

Creating visualizations to understand feature importance

Debugging and validating model behavior

Analyzing fairness and bias

Implementing explainable AI in production

SHAP works with all model types: tree-based models (XGBoost, LightGBM, CatBoost, Random Forest), deep learning models (TensorFlow, PyTorch, Keras), linear models, and black-box models.

When to Use This Skill

Trigger this skill when users ask about:

"Explain which features are most important in my model"

"Generate SHAP plots" (waterfall, beeswarm, bar, scatter, force, heatmap, etc.)

"Why did my model make this prediction?"

"Calculate SHAP values for my model"

"Visualize feature importance using SHAP"

"Debug my model's behavior" or "validate my model"

"Check my model for bias" or "analyze fairness"

"Compare feature importance across models"

"Implement explainable AI" or "add explanations to my model"

"Understand feature interactions"

"Create model interpretation dashboard"

Quick Start Guide

Step 1: Select the Right Explainer

Decision Tree:

Tree-based model? (XGBoost, LightGBM, CatBoost, Random Forest, Gradient Boosting)

- Use shap.TreeExplainer (fast, exact)

Deep neural network? (TensorFlow, PyTorch, Keras, CNNs, RNNs, Transformers)

- Use shap.DeepExplainer or shap.GradientExplainer

Linear model? (Linear/Logistic Regression, GLMs)

- Use shap.LinearExplainer (extremely fast)

Any other model? (SVMs, custom functions, black-box models)

- Use shap.KernelExplainer (model-agnostic but slower)

Unsure?

- Use shap.Explainer (automatically selects best algorithm)

See references/explainers.md for detailed information on all explainer types.

Step 2: Compute SHAP Values

import shap
Example with tree-based model (XGBoost)

import xgboost as xgb
Train model

model = xgb.XGBClassifier().fit(X_train, y_train)
Create explainer

explainer = shap.TreeExplainer(model)
Compute SHAP values

shap_values = explainer(X_test)
The shap_values object contains:

- values: SHAP values (feature attributions)

- base_values: Expected model output (baseline)

- data: Original feature values

Step 3: Visualize Results

For Global Understanding (entire dataset):

# Beeswarm plot - shows feature importance with value distributions
shap.plots.beeswarm(shap_values, max_display=15)
Bar plot - clean summary of feature importance

shap.plots.bar(shap_values)

For Individual Predictions:

# Waterfall plot - detailed breakdown of single prediction
shap.plots.waterfall(shap_values[0])
Force plot - additive force visualization

shap.plots.force(shap_values[0])

For Feature Relationships:

# Scatter plot - feature-prediction relationship
shap.plots.scatter(shap_values[:, "Feature_Name"])
Colored by another feature to show interactions

shap.plots.scatter(shap_values[:, "Age"], color=shap_values[:, "Education"])

See references/plots.md for comprehensive guide on all plot types.

Core Workflows

This skill supports several common workflows. Choose the workflow that matches the current task.

Workflow 1: Basic Model Explanation

Goal: Understand what drives model predictions

Steps:

Train model and create appropriate explainer

Compute SHAP values for test set

Generate global importance plots (beeswarm or bar)

Examine top feature relationships (scatter plots)

Explain specific predictions (waterfall plots)

Example:

# Step 1-2: Setup
explainer = shap.TreeExplainer(model)
shap_values = explainer(X_test)
Step 3: Global importance

shap.plots.beeswarm(shap_values)
Step 4: Feature relationships

shap.plots.scatter(shap_values[:, "Most_Important_Feature"])
Step 5: Individual explanation

shap.plots.waterfall(shap_values[0])

Workflow 2: Model Debugging

Goal: Identify and fix model issues

Steps:

Compute SHAP values

Identify prediction errors

Explain misclassified samples

Check for unexpected feature importance (data leakage)

Validate feature relationships make sense

Check feature interactions

See references/workflows.md for detailed debugging workflow.

Workflow 3: Feature Engineering

Goal: Use SHAP insights to improve features

Steps:

Compute SHAP values for baseline model

Identify nonlinear relationships (candidates for transformation)

Identify feature interactions (candidates for interaction terms)

Engineer new features

Retrain and compare SHAP values

Validate improvements

See references/workflows.md for detailed feature engineering workflow.

Workflow 4: Model Comparison

Goal: Compare multiple models to select best interpretable option

Steps:

Train multiple models

Compute SHAP values for each

Compare global feature importance

Check consistency of feature rankings

Analyze specific predictions across models

Select based on accuracy, interpretability, and consistency

See references/workflows.md for detailed model comparison workflow.

Workflow 5: Fairness and Bias Analysis

Goal: Detect and analyze model bias across demographic groups

Steps:

Identify protected attributes (gender, race, age, etc.)

Compute SHAP values

Compare feature importance across groups

Check protected attribute SHAP importance

Identify proxy features

Implement mitigation strategies if bias found

See references/workflows.md for detailed fairness analysis workflow.

Workflow 6: Production Deployment

Goal: Integrate SHAP explanations into production systems

Steps:

Train and save model

Create and save explainer

Build explanation service

Create API endpoints for predictions with explanations

Implement caching and optimization

Monitor explanation quality

See references/workflows.md for detailed production deployment workflow.

Key Concepts

SHAP Values

Definition: SHAP values quantify each feature's contribution to a prediction, measured as the deviation from the expected model output (baseline).

Properties:

Additivity: SHAP values sum to difference between prediction and baseline

Fairness: Based on Shapley values from game theory

Consistency: If a feature becomes more important, its SHAP value increases

Interpretation:

Positive SHAP value → Feature pushes prediction higher

Negative SHAP value → Feature pushes prediction lower

Magnitude → Strength of feature's impact

Sum of SHAP values → Total prediction change from baseline

Example:

Baseline (expected value): 0.30
Feature contributions (SHAP values):
  Age: +0.15
  Income: +0.10
  Education: -0.05
Final prediction: 0.30 + 0.15 + 0.10 - 0.05 = 0.50

Background Data / Baseline

Purpose: Represents "typical" input to establish baseline expectations

Selection:

Random sample from training data (50-1000 samples)

Or use kmeans to select representative samples

For DeepExplainer/KernelExplainer: 100-1000 samples balances accuracy and speed

Impact: Baseline affects SHAP value magnitudes but not relative importance

Model Output Types

Critical Consideration: Understand what your model outputs

Raw output: For regression or tree margins

Probability: For classification probability

Log-odds: For logistic regression (before sigmoid)

Example: XGBoost classifiers explain margin output (log-odds) by default. To explain probabilities, use model_output="probability" in TreeExplainer.

Common Patterns

Pattern 1: Complete Model Analysis

# 1. Setup
explainer = shap.TreeExplainer(model)
shap_values = explainer(X_test)
2. Global importance

shap.plots.beeswarm(shap_values)
shap.plots.bar(shap_values)
3. Top feature relationships

top_features = X_test.columns[np.abs(shap_values.values).mean(0).argsort()[-5:]]
for feature in top_features:
    shap.plots.scatter(shap_values[:, feature])
4. Example predictions

for i in range(5):
    shap.plots.waterfall(shap_values[i])

Pattern 2: Cohort Comparison

# Define cohorts
cohort1_mask = X_test['Group'] == 'A'
cohort2_mask = X_test['Group'] == 'B'
Compare feature importance

shap.plots.bar({
    "Group A": shap_values[cohort1_mask],
    "Group B": shap_values[cohort2_mask]
})

Pattern 3: Debugging Errors

# Find errors
errors = model.predict(X_test) != y_test
error_indices = np.where(errors)[0]
Explain errors

for idx in error_indices[:5]:
    print(f"Sample {idx}:")
    shap.plots.waterfall(shap_values[idx])
    # Investigate key features
    shap.plots.scatter(shap_values[:, "Suspicious_Feature"])

Performance Optimization

Speed Considerations

Explainer Speed (fastest to slowest):

LinearExplainer - Nearly instantaneous

TreeExplainer - Very fast

DeepExplainer - Fast for neural networks

GradientExplainer - Fast for neural networks

KernelExplainer - Slow (use only when necessary)

PermutationExplainer - Very slow but accurate

Optimization Strategies

For Large Datasets:

# Compute SHAP for subset
shap_values = explainer(X_test[:1000])
Or use batching

batch_size = 100
all_shap_values = []
for i in range(0, len(X_test), batch_size):
    batch_shap = explainer(X_test[i:i+batch_size])
    all_shap_values.append(batch_shap)

For Visualizations:

# Sample subset for plots
shap.plots.beeswarm(shap_values[:1000])
Adjust transparency for dense plots

shap.plots.scatter(shap_values[:, "Feature"], alpha=0.3)

For Production:

# Cache explainer
import joblib
joblib.dump(explainer, 'explainer.pkl')
explainer = joblib.load('explainer.pkl')
Pre-compute for batch predictions

Only compute top N features for API responses

Troubleshooting

Issue: Wrong explainer choice

Problem: Using KernelExplainer for tree models (slow and unnecessary)
Solution: Always use TreeExplainer for tree-based models

Issue: Insufficient background data

Problem: DeepExplainer/KernelExplainer with too few background samples
Solution: Use 100-1000 representative samples

Issue: Confusing units

Problem: Interpreting log-odds as probabilities
Solution: Check model output type; understand whether values are probabilities, log-odds, or raw outputs

Issue: Plots don't display

Problem: Matplotlib backend issues
Solution: Ensure backend is set correctly; use plt.show() if needed

Issue: Too many features cluttering plots

Problem: Default max_display=10 may be too many or too few
Solution: Adjust max_display parameter or use feature clustering

Issue: Slow computation

Problem: Computing SHAP for very large datasets
Solution: Sample subset, use batching, or ensure using specialized explainer (not KernelExplainer)

Integration with Other Tools

Jupyter Notebooks

Interactive force plots work seamlessly

Inline plot display with show=True (default)

Combine with markdown for narrative explanations

MLflow / Experiment Tracking

import mlflow
with mlflow.start_run():
    # Train model
    model = train_model(X_train, y_train)
    # Compute SHAP
    explainer = shap.TreeExplainer(model)
    shap_values = explainer(X_test)
    # Log plots
    shap.plots.beeswarm(shap_values, show=False)
    mlflow.log_figure(plt.gcf(), "shap_beeswarm.png")
    plt.close()
    # Log feature importance metrics
    mean_abs_shap = np.abs(shap_values.values).mean(axis=0)
    for feature, importance in zip(X_test.columns, mean_abs_shap):
        mlflow.log_metric(f"shap_{feature}", importance)

Production APIs

class ExplanationService:
    def __init__(self, model_path, explainer_path):
        self.model = joblib.load(model_path)
        self.explainer = joblib.load(explainer_path)
    def predict_with_explanation(self, X):
        prediction = self.model.predict(X)
        shap_values = self.explainer(X)
        return {
            'prediction': prediction[0],
            'base_value': shap_values.base_values[0],
            'feature_contributions': dict(zip(X.columns, shap_values.values[0]))
        }

Reference Documentation

This skill includes comprehensive reference documentation organized by topic:

references/explainers.md

Complete guide to all explainer classes:

TreeExplainer - Fast, exact explanations for tree-based models

DeepExplainer - Deep learning models (TensorFlow, PyTorch)

KernelExplainer - Model-agnostic (works with any model)

LinearExplainer - Fast explanations for linear models

GradientExplainer - Gradient-based for neural networks

PermutationExplainer - Exact but slow for any model

Includes: Constructor parameters, methods, supported models, when to use, examples, performance considerations.

references/plots.md

Comprehensive visualization guide:

Waterfall plots - Individual prediction breakdowns

Beeswarm plots - Global importance with value distributions

Bar plots - Clean feature importance summaries

Scatter plots - Feature-prediction relationships and interactions

Force plots - Interactive additive force visualizations

Heatmap plots - Multi-sample comparison grids

Violin plots - Distribution-focused alternatives

Decision plots - Multiclass prediction paths

Includes: Parameters, use cases, examples, best practices, plot selection guide.

references/workflows.md

Detailed workflows and best practices:

Basic model explanation workflow

Model debugging and validation

Feature engineering guidance

Model comparison and selection

Fairness and bias analysis

Deep learning model explanation

Production deployment

Time series model explanation

Common pitfalls and solutions

Advanced techniques

MLOps integration

Includes: Step-by-step instructions, code examples, decision criteria, troubleshooting.

references/theory.md

Theoretical foundations:

Shapley values from game theory

Mathematical formulas and properties

Connection to other explanation methods (LIME, DeepLIFT, etc.)

SHAP computation algorithms (Tree SHAP, Kernel SHAP, etc.)

Conditional expectations and baseline selection

Interpreting SHAP values

Interaction values

Theoretical limitations and considerations

Includes: Mathematical foundations, proofs, comparisons, advanced topics.

Usage Guidelines

When to load reference files:

Load explainers.md when user needs detailed information about specific explainer types or parameters

Load plots.md when user needs detailed visualization guidance or exploring plot options

Load workflows.md when user has complex multi-step tasks (debugging, fairness analysis, production deployment)

Load theory.md when user asks about theoretical foundations, Shapley values, or mathematical details

Default approach (without loading references):

Use this SKILL.md for basic explanations and quick start

Provide standard workflows and common patterns

Reference files are available if more detail is needed

Loading references:

# To load reference files, use the Read tool with appropriate file path:
/path/to/shap/references/explainers.md

/path/to/shap/references/plots.md

/path/to/shap/references/workflows.md

/path/to/shap/references/theory.md

Best Practices Summary

Choose the right explainer: Use specialized explainers (TreeExplainer, DeepExplainer, LinearExplainer) when possible; avoid KernelExplainer unless necessary

Start global, then go local: Begin with beeswarm/bar plots for overall understanding, then dive into waterfall/scatter plots for details

Use multiple visualizations: Different plots reveal different insights; combine global (beeswarm) + local (waterfall) + relationship (scatter) views

Select appropriate background data: Use 50-1000 representative samples from training data

Understand model output units: Know whether explaining probabilities, log-odds, or raw outputs

Validate with domain knowledge: SHAP shows model behavior; use domain expertise to interpret and validate

Optimize for performance: Sample subsets for visualization, batch for large datasets, cache explainers in production

Check for data leakage: Unexpectedly high feature importance may indicate data quality issues

Consider feature correlations: Use TreeExplainer's correlation-aware options or feature clustering for redundant features

Remember SHAP shows association, not causation: Use domain knowledge for causal interpretation

Installation

# Basic installation
uv pip install shap
With visualization dependencies

uv pip install shap matplotlib
Latest version

uv pip install -U shap

Dependencies: numpy, pandas, scikit-learn, matplotlib, scipy

Optional: xgboost, lightgbm, tensorflow, torch (depending on model types)

Additional Resources

Official Documentation: https://shap.readthedocs.io/

GitHub Repository: https://github.com/slundberg/shap

Original Paper: Lundberg & Lee (2017) - "A Unified Approach to Interpreting Model Predictions"

Nature MI Paper: Lundberg et al. (2020) - "From local explanations to global understanding with explainable AI for trees"

This skill provides comprehensive coverage of SHAP for model interpretability across all use cases and model types.

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