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

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

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

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

Beeswarm - shows distribution and importance

shap.plots.beeswarm(shap_values)

Bar - clean summary

shap.plots.bar(shap_values)

Individual Predictions

Waterfall - breakdown of single prediction

shap.plots.waterfall(shap_values[0])

Force - additive visualization

shap.plots.force(shap_values[0])

Feature Relationships

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)

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

Common Patterns

Complete Analysis

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

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

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

• Use TreeExplainer for tree models (fast, exact)

• Use 100-1000 background samples for KernelExplainer

• Start global (beeswarm) then go local (waterfall)

• Check model output type (probability vs log-odds)

• Validate with domain knowledge

vs Alternatives

Tool Best For

SHAP Theoretically grounded, all model types

LIME Quick local explanations

Feature Importance Simple tree-based importance

Resources

• Docs: https://shap.readthedocs.io/

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