shap

Original：🇺🇸 English
Translated

Use when "SHAP", "Shapley values", "feature importance", "model explainability", or asking about "explain predictions", "interpretable ML", "feature attribution", "waterfall plot", "beeswarm plot", "model debugging"

4installs
Sourceeyadsibai/ltk
Added on

NPX Install

npx skill4agent add eyadsibai/ltk shap

Tags

Translated version includes tags in frontmatter
shap-analysismodel-explainabilityfeature-importanceml-interpretabilitymodel-debugging

SKILL.md Content

View Translation Comparison →
<!-- Adapted from: claude-scientific-skills/scientific-skills/shap -->

SHAP Model Explainability

Explain ML predictions using Shapley values - feature importance and attribution.

When to Use

  • Explain why a model made specific predictions
  • Calculate feature importance with attribution
  • Debug model behavior and validate predictions
  • Create interpretability plots (waterfall, beeswarm, bar)
  • Analyze model fairness and bias

Quick Start

python
import shap
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)

# Visualize
shap.plots.beeswarm(shap_values)

Choose Explainer

python
# Tree-based models (XGBoost, LightGBM, RF) - FAST
explainer = shap.TreeExplainer(model)

# Deep learning (TensorFlow, PyTorch)
explainer = shap.DeepExplainer(model, background_data)

# Linear models
explainer = shap.LinearExplainer(model, X_train)

# Any model (slower but universal)
explainer = shap.KernelExplainer(model.predict, X_train[:100])

# Auto-select best explainer
explainer = shap.Explainer(model)

Compute SHAP Values

python
# Compute for test set
shap_values = explainer(X_test)

# Access components
shap_values.values      # SHAP values (feature attributions)
shap_values.base_values # Expected model output (baseline)
shap_values.data        # Original feature values

Visualizations

Global Feature Importance

python
# Beeswarm - shows distribution and importance
shap.plots.beeswarm(shap_values)

# Bar - clean summary
shap.plots.bar(shap_values)

Individual Predictions

python
# Waterfall - breakdown of single prediction
shap.plots.waterfall(shap_values[0])

# Force - additive visualization
shap.plots.force(shap_values[0])

Feature Relationships

python
# Scatter - feature vs SHAP value
shap.plots.scatter(shap_values[:, "feature_name"])

# With interaction coloring
shap.plots.scatter(shap_values[:, "Age"], color=shap_values[:, "Income"])

Heatmap (Multiple Samples)

python
shap.plots.heatmap(shap_values[:100])

Common Patterns

Complete Analysis

python
import shap

# 1. Create explainer and compute
explainer = shap.TreeExplainer(model)
shap_values = explainer(X_test)

# 2. Global importance
shap.plots.beeswarm(shap_values)

# 3. Top feature relationships
shap.plots.scatter(shap_values[:, "top_feature"])

# 4. Individual explanation
shap.plots.waterfall(shap_values[0])

Compare Groups

python
# Compare feature importance across groups
group_a = X_test['category'] == 'A'
group_b = X_test['category'] == 'B'

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

Debug Errors

python
# Find misclassified samples
errors = model.predict(X_test) != y_test
error_idx = np.where(errors)[0]

# Explain why they failed
for idx in error_idx[:5]:
    shap.plots.waterfall(shap_values[idx])

Interpret Values

  • Positive SHAP → Feature pushes prediction higher
  • Negative SHAP → Feature pushes prediction lower
  • Magnitude → Strength of impact
  • Sum of SHAP values = Prediction - Baseline
Baseline: 0.30
Age: +0.15
Income: +0.10
Education: -0.05
Prediction: 0.30 + 0.15 + 0.10 - 0.05 = 0.50

Best Practices

  1. Use TreeExplainer for tree models (fast, exact)
  2. Use 100-1000 background samples for KernelExplainer
  3. Start global (beeswarm) then go local (waterfall)
  4. Check model output type (probability vs log-odds)
  5. Validate with domain knowledge

vs Alternatives

ToolBest For
SHAPTheoretically grounded, all model types
LIMEQuick local explanations
Feature ImportanceSimple tree-based importance

Resources