{"id":90988,"date":"2026-05-17T16:34:53","date_gmt":"2026-05-17T16:34:53","guid":{"rendered":"https:\/\/youzum.net\/a-coding-guide-implementing-shap-explainability-workflows-with-explainer-comparisons-maskers-interactions-drift-and-black-box-models\/"},"modified":"2026-05-17T16:34:53","modified_gmt":"2026-05-17T16:34:53","slug":"a-coding-guide-implementing-shap-explainability-workflows-with-explainer-comparisons-maskers-interactions-drift-and-black-box-models","status":"publish","type":"post","link":"https:\/\/youzum.net\/ja\/a-coding-guide-implementing-shap-explainability-workflows-with-explainer-comparisons-maskers-interactions-drift-and-black-box-models\/","title":{"rendered":"A Coding Guide Implementing SHAP Explainability Workflows with Explainer Comparisons, Maskers, Interactions, Drift, and Black-Box Models"},"content":{"rendered":"

In this tutorial, we implement SHAP<\/strong><\/a> workflows as a practical framework for interpreting machine learning models beyond basic feature-importance plots. We start by training tree-based models and then compare different SHAP explainers, including Tree, Exact, Permutation, and Kernel methods, to understand how accuracy and runtime change across model-aware and model-agnostic approaches. We also examine how maskers affect explanations when features are correlated, how interaction values reveal pairwise feature effects, and how link functions alter interpretation between the log-odds and probability spaces. Also, we use Owen values, cohort testing, SHAP-based feature selection, drift monitoring, and custom black-box explanations to build a complete interpretability workflow that can run directly in Google Colab.<\/p>\n

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!pip install -q --upgrade shap xgboost transformers\nimport warnings, time, numpy as np, pandas as pd, matplotlib.pyplot as plt\nfrom scipy import stats\nfrom scipy.cluster import hierarchy\nwarnings.filterwarnings(\"ignore\")\nimport shap, xgboost as xgb\nfrom sklearn.datasets import fetch_california_housing, load_breast_cancer\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score, r2_score\nshap.initjs()\nnp.random.seed(42)\nprint(f\"SHAP: {shap.__version__}n\")\nhousing = fetch_california_housing()\nX = pd.DataFrame(housing.data, columns=housing.feature_names)\ny = pd.Series(housing.target, name=\"MedHouseVal\")\nreg = xgb.XGBRegressor(n_estimators=300, max_depth=5, learning_rate=0.05,\n                     subsample=0.9, random_state=42, n_jobs=-1).fit(X_tr, y_tr)\nprint(f\"Housing regressor R\u00b2 = {reg.score(X_te, y_te):.3f}\")\ndef reg_predict(X):\n   return reg.predict(np.asarray(X))<\/code><\/pre>\n<\/div>\n<\/div>\n
We install the required libraries and import the core tools for SHAP, XGBoost, statistics, visualization, and model evaluation. We load the California housing dataset and train an XGBoost regression model. We also define a clean prediction wrapper so that SHAP can explain the model without running into compatibility issues with bound model methods.<\/p>\n
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print(\"n\" + \"=\"*72)\nprint(\"PART 1: Explainer comparison \u2014 correctness & speed\")\nprint(\"=\"*72)\nX_sample = X_te.iloc[:25]\nbg_small = shap.sample(X_tr, 50, random_state=42)\ndef _wrap_kernel(expl, X, bg_mean):\n   vals = expl.shap_values(X, nsamples=200, silent=True)\n   return shap.Explanation(values=vals, base_values=np.full(len(X), bg_mean),\n                           data=X.values, feature_names=X.columns.tolist())\nruns = {}\nt0 = time.time(); tree_expl = shap.TreeExplainer(reg); sv_tree = tree_expl(X_sample)\nruns[\"Tree (exact, model-aware)\"] = (sv_tree, time.time() - t0)\nt0 = time.time()\nsv_exact = shap.Explainer(reg_predict, bg_small, algorithm=\"exact\")(X_sample)\nruns[\"Exact (model-agnostic)\"] = (sv_exact, time.time() - t0)\nt0 = time.time()\nsv_perm = shap.Explainer(reg_predict, bg_small, algorithm=\"permutation\")(X_sample)\nruns[\"Permutation\"] = (sv_perm, time.time() - t0)\nt0 = time.time()\nke = shap.KernelExplainer(reg_predict, shap.sample(X_tr, 50, random_state=42).values)\nsv_kern = _wrap_kernel(ke, X_sample, ke.expected_value)\nruns[\"Kernel\"] = (sv_kern, time.time() - t0)\nref = sv_tree.values.flatten()\nprint(f\"n{'Method':30s} {'time(s)':>8s}  {'\u03c1 vs Tree':>10s}  {'max|\u0394|':>8s}\")\nfor name, (sv, dt) in runs.items():\n   flat = sv.values.flatten()\n   rho = np.corrcoef(ref, flat)[0, 1]\n   err = np.abs(ref - flat).max()\n   print(f\"{name:30s} {dt:8.2f}  {rho:10.4f}  {err:8.4f}\")\nprint(\"nTakeaway: Tree is the only exact + fast option for tree ensembles.\")\nprint(\"Exact \u2248 Permutation when permutation has enough samples; Kernel is noisier and slowest.\")\nprint(\"n\" + \"=\"*72)\nprint(\"PART 2: Maskers \u2014 Independent vs Partition under correlation\")\nprint(\"=\"*72)\ncorr = X_tr.corr().abs()\ntop_pair = corr.where(np.triu(np.ones_like(corr, dtype=bool), k=1)) \n             .stack().sort_values(ascending=False).head(3)\nprint(\"Top correlated pairs (|\u03c1|):\")\nfor (a, b), v in top_pair.items():\n   print(f\"  {a:10s} \"\u2194\" {b:10s}  |\u03c1| = {v:.3f}\")\nmasker_ind  = shap.maskers.Independent(X_tr, max_samples=100)\nmasker_part = shap.maskers.Partition(X_tr,  max_samples=100)\nsv_ind  = shap.Explainer(reg_predict, masker_ind)(X_sample)\nsv_part = shap.Explainer(reg_predict, masker_part)(X_sample)\na, b = top_pair.index[0]\nprint(f\"nMean |\u03c6| for top-correlated pair ({a}, {b}):\")\nprint(f\"  Independent : {a}={np.abs(sv_ind[:,a].values).mean():.4f}   {b}={np.abs(sv_ind[:,b].values).mean():.4f}\")\nprint(f\"  Partition   : {a}={np.abs(sv_part[:,a].values).mean():.4f}   {b}={np.abs(sv_part[:,b].values).mean():.4f}\")\nprint(\"Partition redistributes credit across correlated features (on-manifold semantics).\")\nfig, axes = plt.subplots(1, 2, figsize=(13, 4))\nplt.sca(axes[0]); shap.plots.bar(sv_ind, show=False);  axes[0].set_title(\"Independent masker\")\nplt.sca(axes[1]); shap.plots.bar(sv_part, show=False); axes[1].set_title(\"Partition masker\")\nplt.tight_layout(); plt.show()<\/code><\/pre>\n<\/div>\n<\/div>\n
We compare multiple SHAP explainers, including Tree, Exact, Permutation, and Kernel, on the same regression model and sample data. We measure each method by runtime, correlation with TreeExplainer, and maximum attribution difference to understand the trade-off between speed and approximation quality. We then study Independent and Partition maskers to see how correlated features receive different attribution credit under different masking assumptions.<\/p>\n
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print(\"n\" + \"=\"*72)\nprint(\"PART 3: Interaction decomposition\")\nprint(\"=\"*72)\ninter = tree_expl.shap_interaction_values(X_te.iloc[:500])\ninter_abs = np.abs(inter).mean(0)\ndiag = np.diagonal(inter_abs).copy()\noff  = inter_abs.copy(); np.fill_diagonal(off, 0)\nmain_share = diag.sum() \/ (diag.sum() + off.sum())\nprint(f\"Total attribution mass: {main_share*100:.1f}% main effects, \"\n     f\"{(1-main_share)*100:.1f}% interactions\")\npairs = [(X.columns[i], X.columns[j], off[i, j])\n        for i in range(X.shape[1]) for j in range(i+1, X.shape[1])]\npairs.sort(key=lambda t: -t[2])\nprint(\"nTop 5 interaction pairs (mean |\u03c6_ij|):\")\nfor a, b, v in pairs[:5]:\n   print(f\"  {a:10s} \u00d7 {b:10s}  \u2192  {v:.4f}\")\nfig, ax = plt.subplots(figsize=(7.5, 6))\nim = ax.imshow(off, cmap=\"viridis\")\nax.set_xticks(range(X.shape[1])); ax.set_xticklabels(X.columns, rotation=45, ha=\"right\")\nax.set_yticks(range(X.shape[1])); ax.set_yticklabels(X.columns)\nplt.colorbar(im, label=\"mean |\u03c6_ij|\"); plt.title(\"Pairwise interaction strength\")\nplt.tight_layout(); plt.show()\na, b, _ = pairs[0]\ni, j = X.columns.get_loc(a), X.columns.get_loc(b)\nxs = X_te.iloc[:500][a].values; cs = X_te.iloc[:500][b].values\nfig, axes = plt.subplots(1, 2, figsize=(13, 4), sharex=True)\naxes[0].scatter(xs, inter[:, i, i], c=cs, s=12, cmap=\"coolwarm\")\naxes[0].set_title(f\"Main effect of {a}\");  axes[0].set_xlabel(a); axes[0].set_ylabel(\"\u03c6_{ii}\")\nsc = axes[1].scatter(xs, 2*inter[:, i, j], c=cs, s=12, cmap=\"coolwarm\")\naxes[1].set_title(f\"Interaction {a} \u00d7 {b}\"); axes[1].set_xlabel(a); axes[1].set_ylabel(\"2\u00b7\u03c6_{ij}\")\nplt.colorbar(sc, ax=axes[1], label=b); plt.tight_layout(); plt.show()\nprint(\"n\" + \"=\"*72)\nprint(\"PART 4: Link functions \u2014 logit vs probability space\")\nprint(\"=\"*72)\ncancer = load_breast_cancer()\nXc = pd.DataFrame(cancer.data, columns=cancer.feature_names)\nyc = pd.Series(cancer.target)\nclf = xgb.XGBClassifier(n_estimators=300, max_depth=4, learning_rate=0.05,\n                       eval_metric=\"logloss\", random_state=42).fit(Xc_tr, yc_tr)\nprint(f\"AUC = {roc_auc_score(yc_te, clf.predict_proba(Xc_te)[:,1]):.3f}\")\nexpl_logit = shap.TreeExplainer(clf)\nsv_logit   = expl_logit(Xc_te)\nexpl_prob  = shap.TreeExplainer(clf, Xc_tr.sample(100, random_state=42),\n                               model_output=\"probability\")\nsv_prob    = expl_prob(Xc_te)\nprint(f\"nSample 0 reconstruction (\u03c6 should sum to f - E[f]):\")\nprint(f\"  log-odds : base + \u03a3\u03c6 = {sv_logit.base_values[0] + sv_logit.values[0].sum():+.3f}\")\nprint(f\"  prob     : base + \u03a3\u03c6 = {sv_prob.base_values[0]  + sv_prob.values[0].sum():.3f} \"\n     f\"(model proba = {clf.predict_proba(Xc_te.iloc[[0]])[0,1]:.3f})\")\nfig, axes = plt.subplots(1, 2, figsize=(15, 5))\nplt.sca(axes[0]); shap.plots.waterfall(sv_logit[0], max_display=8, show=False); axes[0].set_title(\"Log-odds space\")\nplt.sca(axes[1]); shap.plots.waterfall(sv_prob[0],  max_display=8, show=False); axes[1].set_title(\"Probability space\")\nplt.tight_layout(); plt.show()<\/code><\/pre>\n<\/div>\n<\/div>\n
We calculate SHAP interaction values to separate main feature effects from pairwise interaction effects in the housing model. We identify the strongest interaction pairs and visualize their attribution strength using heatmaps and scatter plots. We then move to a classification task and compare SHAP explanations in log-odds and probability spaces using a breast cancer classifier.<\/p>\n
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print(\"n\" + \"=\"*72)\nprint(\"PART 5: Owen values from a correlation-based feature hierarchy\")\nprint(\"=\"*72)\nD = 1 - X_tr.corr().abs().values\nnp.fill_diagonal(D, 0)\ncondensed = D[np.triu_indices_from(D, k=1)]\nlinkage = hierarchy.linkage(condensed, method=\"average\")\nmasker_owen = shap.maskers.Partition(X_tr, clustering=linkage, max_samples=100)\nsv_owen = shap.Explainer(reg_predict, masker_owen)(X_sample)\nfig, axes = plt.subplots(1, 2, figsize=(14, 4.5))\nhierarchy.dendrogram(linkage, labels=X.columns.tolist(), ax=axes[0])\naxes[0].set_title(\"Feature hierarchy (1 \u2212 |\u03c1|)\")\nplt.sca(axes[1]); shap.plots.bar(sv_owen.abs.mean(0), show=False)\naxes[1].set_title(\"Owen values (cluster-aware)\")\nplt.tight_layout(); plt.show()\nprint(\"n\" + \"=\"*72)\nprint(\"PART 6: Cohort comparison with bootstrap CIs and hypothesis tests\")\nprint(\"=\"*72)\nsv_all = tree_expl(X_te)\nq1, q3 = X_te[\"MedInc\"].quantile([0.25, 0.75])\nlow  = (X_te[\"MedInc\"] <= q1).values\nhigh = (X_te[\"MedInc\"] >= q3).values\ndef boot_ci(v, B=1000, seed=0):\n   rng = np.random.default_rng(seed); n = len(v)\n   return np.percentile([np.abs(v[rng.integers(0, n, n)]).mean() for _ in range(B)], [2.5, 97.5])\nprint(f\"nLow income cohort n={low.sum()},  High income cohort n={high.sum()}\")\nprint(f\"{'Feature':12s} {'low \u03bc|\u03c6|':>9s} {'CI_low':>14s}   {'high \u03bc|\u03c6|':>10s} {'CI_high':>14s}   {'Welch p':>10s}\")\nfor j, col in enumerate(X.columns):\n   lv, hv = sv_all.values[low, j], sv_all.values[high, j]\n   ci_l, ci_h = boot_ci(lv, seed=j), boot_ci(hv, seed=j+100)\n   _, p = stats.ttest_ind(np.abs(lv), np.abs(hv), equal_var=False)\n   star = \" *\" if p < 0.001 else \"\"\n   print(f\"{col:12s} {np.abs(lv).mean():9.4f} [{ci_l[0]:.3f},{ci_l[1]:.3f}]   \"\n         f\"{np.abs(hv).mean():10.4f} [{ci_h[0]:.3f},{ci_h[1]:.3f}]   {p:10.2e}{star}\")<\/code><\/pre>\n<\/div>\n<\/div>\n
We create a correlation-based feature hierarchy and use a Partition masker to compute Owen values that respect feature coalitions. We visualize both the feature hierarchy and the resulting cluster-aware attribution importance. We also compare low-income and high-income cohorts using bootstrap confidence intervals and Welch\u2019s t-test to identify features whose SHAP attribution patterns differ statistically.<\/p>\n
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print(\"n\" + \"=\"*72)\nprint(\"PART 7: SHAP-driven feature selection\")\nprint(\"=\"*72)\nsv_tr = tree_expl(X_tr.sample(2000, random_state=42))\nrank  = pd.Series(np.abs(sv_tr.values).mean(0), index=X.columns).sort_values(ascending=False)\nprint(\"Importance ranking:n\", rank.round(4).to_string())\ncurve = {}\nfor k in range(1, len(X.columns) + 1):\n   feats = rank.head(k).index.tolist()\n   m = xgb.XGBRegressor(n_estimators=300, max_depth=5, learning_rate=0.05,\n                        random_state=42, n_jobs=-1).fit(X_tr[feats], y_tr)\n   curve[k] = r2_score(y_te, m.predict(X_te[feats]))\nplt.figure(figsize=(8, 4))\nplt.plot(list(curve.keys()), list(curve.values()), \"-o\")\nplt.xlabel(\"Top-k features by mean |SHAP|\"); plt.ylabel(\"Test R\u00b2\")\nplt.title(\"Validation curve for SHAP-based feature selection\")\nplt.grid(alpha=0.3); plt.tight_layout(); plt.show()\nprint(\"n\" + \"=\"*72)\nprint(\"PART 8: Drift detection via KS tests on SHAP distributions\")\nprint(\"=\"*72)\nref_mask   = (X_te[\"MedInc\"] <= X_te[\"MedInc\"].quantile(0.7)).values\nshift_mask = ~ref_mask\nsv_ref   = sv_all.values[ref_mask]\nsv_shift = sv_all.values[shift_mask]\nprint(f\"{'feature':12s} {'KS':>6s} {'p':>10s}   verdict\")\nfor j, col in enumerate(X.columns):\n   ks, p = stats.ks_2samp(sv_ref[:, j], sv_shift[:, j])\n   verdict = \"DRIFT\" if p < 0.01 else \"ok\"\n   print(f\"{col:12s} {ks:6.3f} {p:10.2e}   {verdict}\")\nprint(\"n\" + \"=\"*72)\nprint(\"PART 9: Explaining an arbitrary black-box function\")\nprint(\"=\"*72)\ndef black_box(X):\n   X = np.asarray(X, dtype=float)\n   return (2*np.sin(X[:,0]) + 0.5*X[:,1]**2 - X[:,2]*X[:,3]\n           + np.where(X[:,4] > 0, 1.0, -1.0))\nX_bb = np.random.default_rng(0).standard_normal((500, 5))\nnames = [f\"x{i}\" for i in range(5)]\nmasker_bb = shap.maskers.Independent(X_bb, max_samples=100)\nsv_perm_bb  = shap.Explainer(black_box, masker_bb, feature_names=names,\n                            algorithm=\"permutation\")(X_bb[:100])\nsv_exact_bb = shap.Explainer(black_box, masker_bb, feature_names=names,\n                            algorithm=\"exact\")(X_bb[:100])\nprint(f\"Permutation vs Exact correlation: \"\n     f\"\u03c1 = {np.corrcoef(sv_perm_bb.values.flatten(), sv_exact_bb.values.flatten())[0,1]:.4f}\")\nshap.plots.beeswarm(sv_exact_bb, show=False)\nplt.title(\"Custom function \u2014 Exact Shapley values\"); plt.tight_layout(); plt.show()<\/code><\/pre>\n<\/div>\n<\/div>\n
We rank features by mean absolute SHAP values and retrain models with the top-k features to build a validation curve for SHAP-based feature selection. We then use KS tests on SHAP value distributions to detect attribution drift between reference and shifted groups. Also, we explain a fully custom black-box Python function using permutation and exact SHAP explainers, showing that SHAP can work beyond standard ML models.<\/p>\n
In conclusion, we built a strong, hands-on understanding of how SHAP supports advanced model explanation, validation, and monitoring. We saw how different explainers behave under the same model, how correlated features influence attribution, and how interaction values help us separate main effects from feature-pair effects. We also used SHAP values for practical tasks such as comparing cohorts, selecting important features, detecting attribution drift, and explaining arbitrary black-box functions. Also, we created a reusable interpretability pipeline that helps us move from simple model explanations to deeper, production-oriented analysis of model behavior.<\/p>\n\n
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The post A Coding Guide Implementing SHAP Explainability Workflows with Explainer Comparisons, Maskers, Interactions, Drift, and Black-Box Models<\/a> appeared first on MarkTechPost<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"
In this tutorial, we implement SHAP workflows as a practical framework for interpreting machine learning models beyond basic feature-importance plots. We start by training tree-based models and then compare different SHAP explainers, including Tree, Exact, Permutation, and Kernel methods, to understand how accuracy and runtime change across model-aware and model-agnostic approaches. We also examine how maskers affect explanations when features are correlated, how interaction values reveal pairwise feature effects, and how link functions alter interpretation between the log-odds and probability spaces. Also, we use Owen values, cohort testing, SHAP-based feature selection, drift monitoring, and custom black-box explanations to build a complete interpretability workflow that can run directly in Google Colab. Copy CodeCopiedUse a different Browser !pip install -q –upgrade shap xgboost transformers import warnings, time, numpy as np, pandas as pd, matplotlib.pyplot as plt from scipy import stats from scipy.cluster import hierarchy warnings.filterwarnings(“ignore”) import shap, xgboost as xgb from sklearn.datasets import fetch_california_housing, load_breast_cancer from sklearn.model_selection import train_test_split from sklearn.metrics import roc_auc_score, r2_score shap.initjs() np.random.seed(42) print(f”SHAP: {shap.__version__}n”) housing = fetch_california_housing() X = pd.DataFrame(housing.data, columns=housing.feature_names) y = pd.Series(housing.target, name=”MedHouseVal”) reg = xgb.XGBRegressor(n_estimators=300, max_depth=5, learning_rate=0.05, subsample=0.9, random_state=42, n_jobs=-1).fit(X_tr, y_tr) print(f”Housing regressor R\u00b2 = {reg.score(X_te, y_te):.3f}”) def reg_predict(X): return reg.predict(np.asarray(X)) We install the required libraries and import the core tools for SHAP, XGBoost, statistics, visualization, and model evaluation. We load the California housing dataset and train an XGBoost regression model. We also define a clean prediction wrapper so that SHAP can explain the model without running into compatibility issues with bound model methods. Copy CodeCopiedUse a different Browser print(“n” + “=”*72) print(“PART 1: Explainer comparison \u2014 correctness & speed”) print(“=”*72) X_sample = X_te.iloc[:25] bg_small = shap.sample(X_tr, 50, random_state=42) def _wrap_kernel(expl, X, bg_mean): vals = expl.shap_values(X, nsamples=200, silent=True) return shap.Explanation(values=vals, base_values=np.full(len(X), bg_mean), data=X.values, feature_names=X.columns.tolist()) runs = {} t0 = time.time(); tree_expl = shap.TreeExplainer(reg); sv_tree = tree_expl(X_sample) runs[“Tree (exact, model-aware)”] = (sv_tree, time.time() – t0) t0 = time.time() sv_exact = shap.Explainer(reg_predict, bg_small, algorithm=”exact”)(X_sample) runs[“Exact (model-agnostic)”] = (sv_exact, time.time() – t0) t0 = time.time() sv_perm = shap.Explainer(reg_predict, bg_small, algorithm=”permutation”)(X_sample) runs[“Permutation”] = (sv_perm, time.time() – t0) t0 = time.time() ke = shap.KernelExplainer(reg_predict, shap.sample(X_tr, 50, random_state=42).values) sv_kern = _wrap_kernel(ke, X_sample, ke.expected_value) runs[“Kernel”] = (sv_kern, time.time() – t0) ref = sv_tree.values.flatten() print(f”n{‘Method’:30s} {‘time(s)’:>8s} {‘\u03c1 vs Tree’:>10s} {‘max|\u0394|’:>8s}”) for name, (sv, dt) in runs.items(): flat = sv.values.flatten() rho = np.corrcoef(ref, flat)[0, 1] err = np.abs(ref – flat).max() print(f”{name:30s} {dt:8.2f} {rho:10.4f} {err:8.4f}”) print(“nTakeaway: Tree is the only exact + fast option for tree ensembles.”) print(“Exact \u2248 Permutation when permutation has enough samples; Kernel is noisier and slowest.”) print(“n” + “=”*72) print(“PART 2: Maskers \u2014 Independent vs Partition under correlation”) print(“=”*72) corr = X_tr.corr().abs() top_pair = corr.where(np.triu(np.ones_like(corr, dtype=bool), k=1)) .stack().sort_values(ascending=False).head(3) print(“Top correlated pairs (|\u03c1|):”) for (a, b), v in top_pair.items(): print(f” {a:10s} {b:10s} |\u03c1| = {v:.3f}”) masker_ind = shap.maskers.Independent(X_tr, max_samples=100) masker_part = shap.maskers.Partition(X_tr, max_samples=100) sv_ind = shap.Explainer(reg_predict, masker_ind)(X_sample) sv_part = shap.Explainer(reg_predict, masker_part)(X_sample) a, b = top_pair.index[0] print(f”nMean |\u03c6| for top-correlated pair ({a}, {b}):”) print(f” Independent : {a}={np.abs(sv_ind[:,a].values).mean():.4f} {b}={np.abs(sv_ind[:,b].values).mean():.4f}”) print(f” Partition : {a}={np.abs(sv_part[:,a].values).mean():.4f} {b}={np.abs(sv_part[:,b].values).mean():.4f}”) print(“Partition redistributes credit across correlated features (on-manifold semantics).”) fig, axes = plt.subplots(1, 2, figsize=(13, 4)) plt.sca(axes[0]); shap.plots.bar(sv_ind, show=False); axes[0].set_title(“Independent masker”) plt.sca(axes[1]); shap.plots.bar(sv_part, show=False); axes[1].set_title(“Partition masker”) plt.tight_layout(); plt.show() We compare multiple SHAP explainers, including Tree, Exact, Permutation, and Kernel, on the same regression model and sample data. We measure each method by runtime, correlation with TreeExplainer, and maximum attribution difference to understand the trade-off between speed and approximation quality. We then study Independent and Partition maskers to see how correlated features receive different attribution credit under different masking assumptions. Copy CodeCopiedUse a different Browser print(“n” + “=”*72) print(“PART 3: Interaction decomposition”) print(“=”*72) inter = tree_expl.shap_interaction_values(X_te.iloc[:500]) inter_abs = np.abs(inter).mean(0) diag = np.diagonal(inter_abs).copy() off = inter_abs.copy(); np.fill_diagonal(off, 0) main_share = diag.sum() \/ (diag.sum() + off.sum()) print(f”Total attribution mass: {main_share*100:.1f}% main effects, ” f”{(1-main_share)*100:.1f}% interactions”) pairs = [(X.columns[i], X.columns[j], off[i, j]) for i in range(X.shape[1]) for j in range(i+1, X.shape[1])] pairs.sort(key=lambda t: -t[2]) print(“nTop 5 interaction pairs (mean |\u03c6_ij|):”) for a, b, v in pairs[:5]: print(f” {a:10s} \u00d7 {b:10s} \u2192 {v:.4f}”) fig, ax = plt.subplots(figsize=(7.5, 6)) im = ax.imshow(off, cmap=”viridis”) ax.set_xticks(range(X.shape[1])); ax.set_xticklabels(X.columns, rotation=45, ha=”right”) ax.set_yticks(range(X.shape[1])); ax.set_yticklabels(X.columns) plt.colorbar(im, label=”mean |\u03c6_ij|”); plt.title(“Pairwise interaction strength”) plt.tight_layout(); plt.show() a, b, _ = pairs[0] i, j = X.columns.get_loc(a), X.columns.get_loc(b) xs = X_te.iloc[:500][a].values; cs = X_te.iloc[:500][b].values fig, axes = plt.subplots(1, 2, figsize=(13, 4), sharex=True) axes[0].scatter(xs, inter[:, i, i], c=cs, s=12, cmap=”coolwarm”) axes[0].set_title(f”Main effect of {a}”); axes[0].set_xlabel(a); axes[0].set_ylabel(“\u03c6_{ii}”) sc = axes[1].scatter(xs, 2*inter[:, i, j], c=cs, s=12, cmap=”coolwarm”) axes[1].set_title(f”Interaction {a} \u00d7 {b}”); axes[1].set_xlabel(a); axes[1].set_ylabel(“2\u00b7\u03c6_{ij}”) plt.colorbar(sc, ax=axes[1], label=b); plt.tight_layout(); plt.show() print(“n” + “=”*72) print(“PART 4: Link functions \u2014 logit vs probability space”) print(“=”*72) cancer = load_breast_cancer() Xc = pd.DataFrame(cancer.data, columns=cancer.feature_names) yc = pd.Series(cancer.target) clf = xgb.XGBClassifier(n_estimators=300, max_depth=4, learning_rate=0.05, eval_metric=”logloss”, random_state=42).fit(Xc_tr, yc_tr) print(f”AUC = {roc_auc_score(yc_te, clf.predict_proba(Xc_te)[:,1]):.3f}”) expl_logit = shap.TreeExplainer(clf) sv_logit = expl_logit(Xc_te) expl_prob = shap.TreeExplainer(clf, Xc_tr.sample(100, random_state=42), model_output=”probability”) sv_prob = expl_prob(Xc_te) print(f”nSample 0 reconstruction (\u03c6 should sum to f – E[f]):”) print(f” log-odds : base + \u03a3\u03c6 = {sv_logit.base_values[0] + sv_logit.values[0].sum():+.3f}”) print(f” prob : base + \u03a3\u03c6 = {sv_prob.base_values[0] + sv_prob.values[0].sum():.3f} ” f”(model proba = {clf.predict_proba(Xc_te.iloc[[0]])[0,1]:.3f})”) fig, axes = plt.subplots(1, 2, figsize=(15, 5)) plt.sca(axes[0]); shap.plots.waterfall(sv_logit[0], max_display=8, show=False); axes[0].set_title(“Log-odds space”) plt.sca(axes[1]); shap.plots.waterfall(sv_prob[0], max_display=8, show=False); axes[1].set_title(“Probability space”) plt.tight_layout(); plt.show() We calculate SHAP interaction values to separate main feature effects from pairwise interaction effects in the housing model. We identify the strongest interaction pairs and visualize their attribution strength using heatmaps and scatter plots. We then move to a classification task and compare SHAP explanations in log-odds and probability spaces using a breast cancer classifier. Copy CodeCopiedUse a different Browser print(“n” + “=”*72) print(“PART 5: Owen values from a correlation-based feature hierarchy”) print(“=”*72) D = 1 – X_tr.corr().abs().values np.fill_diagonal(D, 0) condensed = D[np.triu_indices_from(D, k=1)] linkage = hierarchy.linkage(condensed, method=”average”) masker_owen = shap.maskers.Partition(X_tr, clustering=linkage, max_samples=100) sv_owen = shap.Explainer(reg_predict, masker_owen)(X_sample) fig, axes = plt.subplots(1, 2, figsize=(14, 4.5)) hierarchy.dendrogram(linkage, labels=X.columns.tolist(), ax=axes[0]) axes[0].set_title(“Feature hierarchy (1 \u2212 |\u03c1|)”) plt.sca(axes[1]); shap.plots.bar(sv_owen.abs.mean(0), show=False) axes[1].set_title(“Owen values (cluster-aware)”) plt.tight_layout(); plt.show() print(“n” + “=”*72) print(“PART 6: Cohort comparison with bootstrap CIs and hypothesis tests”) print(“=”*72) sv_all = tree_expl(X_te) q1, q3 = X_te[“MedInc”].quantile([0.25, 0.75]) low = (X_te[“MedInc”] <= q1).values high = (X_te[“MedInc”] >= q3).values def boot_ci(v, B=1000, seed=0):<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_pvb_checkbox_block_on_post":false,"footnotes":""},"categories":[52,5,7,1],"tags":[],"class_list":["post-90988","post","type-post","status-publish","format-standard","hentry","category-ai-club","category-committee","category-news","category-uncategorized","pmpro-has-access"],"acf":[],"yoast_head":"\nA Coding Guide Implementing SHAP Explainability Workflows with Explainer Comparisons, Maskers, Interactions, Drift, and Black-Box Models - 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