Question

Examples of plan shap

Answer 1

Do you mean SHAP (the ML explanation library) or “plan shape” (types/ layouts of a floor plan)? I can give examples for either — below are short examples for both. Tell me which you meant and I’ll expand.

If you meant SHAP (SHapley Additive exPlanations) — quick practical examples

- Tree model (XGBoost / LightGBM / sklearn RandomForest)
- Use shap.TreeExplainer which is fast and exact (for tree ensembles).
- Example (Python):
```
import xgboost as xgb, shap
model = xgb.XGBRegressor().fit(X_train, y_train)
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test) # global feature importance + effect
shap.plots.waterfall(explainer.expected_value, shap_values[0], X_test.iloc[0])
```
- What you get: summary plot ranking features by average absolute SHAP; waterfall or force plots for single-prediction explanations; dependence plots to show interaction and nonlinear effects.

- Black‑box model (any model) — KernelExplainer
- Use KernelExplainer with a small background dataset (slow; good for ~dozens of features).
- Example:
```
import shap
explainer = shap.KernelExplainer(model.predict, shap.sample(X_train, 50))
shap_values = explainer.shap_values(X_test.iloc[:10])
shap.summary_plot(shap_values, X_test.iloc[:10])
```
- Notes: pick a representative background; runtime can be high; try to limit features or use grouping.

- Use-case: feature selection / monitoring plan
- Plan: compute SHAP for a validation set → rank features by mean(|SHAP|) → inspect top features and dependence plots → iterate (remove or combine low‑impact features, check model performance).
- Production monitoring: store per‑prediction SHAP summaries (e.g., top-3 contributors) and track shifts in average SHAP per feature over time; alert when contribution patterns change.

- Interpretability tips & pitfalls
- SHAP values are additive and sum to model output (relative to expected value) — makes per-prediction decomposition intuitive.
- Be careful with correlated features: SHAP allocations can split importance arbitrarily among correlated variables.
- Choice of background distribution matters (affects kernel explainer baselines).
- For very high-dimensional data consider feature grouping or dimensionality reduction before SHAP.

If you meant “plan shape” (floor-plan/layout examples)

- Open-plan
- Large shared living/kitchen/dining area with few interior walls. Good for social spaces, natural light.
- L-shaped plan
- Two wings meeting at a corner (e.g., living + kitchen in one wing, bedrooms in other) — good separation of public/private spaces.
- U-shaped plan
- Rooms wrap around a courtyard or central garden — provides privacy and a protected central outdoor space.
- Linear / shotgun plan
- Rooms arranged in sequence along a corridor or axis — efficient for narrow lots.
- Split-level
- Staggered floor levels (half flights of stairs) to separate functions without full floors.
- Courtyard / atrium plan
- Central open space around which rooms are organized — improves ventilation and daylighting.

Which of these did you mean? If SHAP, tell me the model type (tree, neural net, black box) and your dataset size and I’ll give a tailored code example and a step‑by‑step plan for applying SHAP in your project. If plan shape, tell me the context (home, office, small site constraints) and I’ll give more specific plan options and sketches.