shap

name	shap
description	Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
license	MIT license
metadata	{"skill-author":"K-Dense Inc."}

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:

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

   • Use shap.TreeExplainer (fast, exact)

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

   • Use shap.DeepExplainer or shap.GradientExplainer

3. Linear model? (Linear/Logistic Regression, GLMs)

   • Use shap.LinearExplainer (extremely fast)

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

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

5. 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

# Output shape is model-family-dependent:
# - XGBoost / LightGBM / gradient boosting: values is 2D (n_samples, n_features)
# - scikit-learn forests (RandomForest, etc.): values is 3D
#   (n_samples, n_features, n_classes), carrying a trailing class axis
# Select a class before plotting 3D outputs, e.g. shap_values[..., 1]

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])

Classifier note: For scikit-learn classifiers (e.g. RandomForestClassifier), explainer(X) returns a 3D Explanation (n_samples, n_features, n_classes). Select a class before passing to a plot, otherwise these calls error. For a single prediction use shap_values[0, :, 1]; for the global plots above use shap_values[..., 1] (the example here uses an XGBoost classifier, whose output is already 2D).

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:

1. Train model and create appropriate explainer
2. Compute SHAP values for test set
3. Generate global importance plots (beeswarm or bar)
4. Examine top feature relationships (scatter plots)
5. Explain specific predictions (waterfall plots)

Example:

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

# For sklearn classifiers shap_values is 3D (carries a trailing class axis);
# select a class first, e.g. shap_values = shap_values[..., 1]

# 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:

1. Compute SHAP values
2. Identify prediction errors
3. Explain misclassified samples
4. Check for unexpected feature importance (data leakage)
5. Validate feature relationships make sense
6. Check feature interactions

See references/workflows.md for detailed debugging workflow.

Workflow 3: Feature Engineering

Goal: Use SHAP insights to improve features

Steps:

1. Compute SHAP values for baseline model
2. Identify nonlinear relationships (candidates for transformation)
3. Identify feature interactions (candidates for interaction terms)
4. Engineer new features
5. Retrain and compare SHAP values
6. 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:

1. Train multiple models
2. Compute SHAP values for each
3. Compare global feature importance
4. Check consistency of feature rankings
5. Analyze specific predictions across models
6. 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:

1. Identify protected attributes (gender, race, age, etc.)
2. Compute SHAP values
3. Compare feature importance across groups
4. Check protected attribute SHAP importance
5. Identify proxy features
6. 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:

1. Train and save model
2. Create and save explainer
3. Build explanation service
4. Create API endpoints for predictions with explanations
5. Implement caching and optimization
6. 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: The background choice can affect SHAP value magnitudes, signs, and feature rankings

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 and gradient-boosting classifiers explain margin output (log-odds) by default, while scikit-learn forests (e.g. RandomForest) explain probability. To explain probabilities, construct TreeExplainer with model_output="probability", data=background, and feature_perturbation="interventional" (the no-background tree_path_dependent default only supports model_output="raw").

Common Patterns

Pattern 1: Complete Model Analysis

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

# For sklearn classifiers shap_values is 3D (trailing class axis);
# select a class first so the plots and .values math below stay 2D
# shap_values = shap_values[..., 1]

# 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
# .values converts the pandas boolean Series to a numpy array;
# indexing an Explanation with a Series directly raises a ValueError.
cohort1_mask = X_test['Group'] == 'A'
cohort2_mask = X_test['Group'] == 'B'

# Compare feature importance
shap.plots.bar({
    "Group A": shap_values[cohort1_mask.values],
    "Group B": shap_values[cohort2_mask.values]
})

Pattern 3: Debugging Errors

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

# For sklearn classifiers shap_values is 3D (trailing class axis);
# select a class first, e.g. shap_values = shap_values[..., 1]

# 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):

1. LinearExplainer - Nearly instantaneous
2. TreeExplainer - Very fast
3. DeepExplainer - Fast for neural networks
4. GradientExplainer - Fast for neural networks
5. KernelExplainer - Slow (use only when necessary)
6. PermutationExplainer - Slow, model-agnostic approximation

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 - Model-agnostic approximation, 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

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

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

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

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

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

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

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

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

9. Consider feature correlations: Use TreeExplainer's feature_perturbation choices (interventional vs tree_path_dependent) or feature clustering for redundant features

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

Installation

# Core installation (no plotting; shap.plots.* will be unavailable)
uv pip install shap

# With visualization dependencies (required for shap.plots.*)
uv pip install "shap[plots]"

# Latest version
uv pip install -U "shap[plots]"

Dependencies: numpy, pandas, scikit-learn, scipy

Optional: matplotlib and ipython (via the plots extra, required for shap.plots.*); xgboost, lightgbm, tensorflow, torch (depending on model types)

Additional Resources

• Official Documentation: https://shap.readthedocs.io/
• GitHub Repository: https://github.com/shap/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.