> ## Documentation Index > Fetch the complete documentation index at: https://docs.priorlabs.ai/llms.txt > Use this file to discover all available pages before exploring further. # Interpreting Results with TabPFN > Learn how to use the standard interpretability toolkit with TabPFN for explainable results

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By Prior Labs

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Open in Colab

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*With SHAP values and partial dependence plots.* A strong classifier is only useful in practice if you can explain what it is doing. TabPFN works with the standard interpretability toolkit, so the same SHAP and partial-dependence techniques you would use on any model apply directly. Working with a coronary-disease dataset, this notebook covers global feature importance, how predictions respond to each feature, and how to explain individual patient predictions. ## Setup *Installing TabPFN, its interpretability extensions, and SHAP.* `tabpfn_extensions` provides the interpretability helpers used here; `shap` provides the explainer and plots. ```python theme={null} !pip install tabpfn tabpfn_extensions scikit-learn shap ``` ```python theme={null} import os from google.colab import userdata import warnings from pathlib import Path import matplotlib import matplotlib.pyplot as plt import numpy as np import shap from sklearn.datasets import fetch_openml from sklearn.metrics import roc_auc_score from sklearn.model_selection import train_test_split from tabpfn_extensions import TabPFNClassifier from tabpfn_extensions.interpretability import pdp as tabpfn_pdp warnings.filterwarnings("ignore") ``` ```python theme={null} os.environ["TABPFN_TOKEN"] = userdata.get('TABPFN_TOKEN') ``` ## Loading the Data *A coronary-disease dataset, relabelled for readable explanations.* We use the heart-statlog dataset. Interpretability is only as clear as the feature names, so we rename the columns to plain language and keep a decoding map for categorical values. This makes every plot below far easier to read. ### Readable names and categories *Mapping column names and codes to human-readable labels.* ```python theme={null} # @title RENAME = { "age": "Age", "sex": "Sex", "chest": "Chest pain type", "resting_blood_pressure": "Resting BP", "serum_cholestoral": "Cholesterol", "fasting_blood_sugar": "High fasting sugar", "resting_electrocardiographic_results": "Resting ECG", "maximum_heart_rate_achieved": "Max heart rate", "exercise_induced_angina": "Exercise angina", "oldpeak": "ST depression", "slope": "ST slope", "number_of_major_vessels": "Blocked vessels", "thal": "Thallium scan", } DECODE = { "Sex": {0: "female", 1: "male"}, "Chest pain type": {1: "typical angina", 2: "atypical angina", 3: "non-anginal", 4: "asymptomatic"}, "High fasting sugar": {0: "no", 1: "yes"}, "Resting ECG": {0: "normal", 1: "ST-T abnormality", 2: "LV hypertrophy"}, "Exercise angina": {0: "no", 1: "yes"}, "ST slope": {1: "upsloping", 2: "flat", 3: "downsloping"}, "Thallium scan": {3: "normal", 6: "fixed defect", 7: "reversible defect"}, } CATEGORICAL = ["Sex", "Chest pain type", "High fasting sugar", "Resting ECG", "Exercise angina", "ST slope", "Thallium scan"] CONTINUOUS = ["Age", "Resting BP", "Cholesterol", "Max heart rate", "ST depression", "Blocked vessels"] ``` ```python theme={null} raw = fetch_openml("heart-statlog", version=1, as_frame=True) X = raw.data.astype(float).rename(columns=RENAME) y = (raw.target == "present").astype(int) feature_names = list(X.columns) cat_idx = [feature_names.index(c) for c in CATEGORICAL] X_train, X_test, y_train, y_test = train_test_split( X, y, train_size=200, test_size=70, random_state=42, stratify=y ) ``` ## Fit and Predict *Training the classifier we are going to explain.* We fit `TabPFNClassifier`, passing the categorical feature indices so the model treats them correctly, and keep the predicted probabilities for the test set. ```python theme={null} clf = TabPFNClassifier(fit_mode="fit_with_cache", categorical_features_indices=cat_idx) clf.fit(X_train.values, y_train.values) proba_test = clf.predict_proba(X_test.values)[:, 1] ``` ## Global Feature Importance *SHAP values across the whole test set.* A permutation SHAP explainer attributes each prediction to its features. The beeswarm plot summarises every patient at once: horizontal position shows whether a feature pushed risk up or down, colour shows whether the feature value was high or low. ```python theme={null} predict = lambda A: clf.predict_proba(np.asarray(A))[:, 1] background = shap.sample(X_train, 50, random_state=42) explainer = shap.PermutationExplainer(predict, background) sv = explainer(X_test, max_evals=2 * len(feature_names) + 1) sv.display_data = X_test.values shap.plots.beeswarm(sv, max_display=len(feature_names), show=False) fig = plt.gcf() fig.set_size_inches(9, 6) fig.suptitle( "Global drivers of coronary-disease risk — each dot is a patient\n" "right = pushed toward disease, red = high feature value", fontsize=11, ) plt.show() ``` ![Global Feature Importance](https://raw.githubusercontent.com/PriorLabs/tabpfn-cookbook/main/visuals/interpret_results/plot-01.png) ## Partial Dependence *How average predicted risk moves with each measurement.* Where SHAP shows attribution, partial dependence shows shape. For the top continuous features we trace how the average predicted probability changes as we sweep each one across its range. ```python theme={null} mean_abs = np.abs(sv.values).mean(0) ranking = np.argsort(-mean_abs) cont_ranked = [i for i in ranking if feature_names[i] in CONTINUOUS][:4] print(f"Partial dependence for: {[feature_names[i] for i in cont_ranked]}") disp = tabpfn_pdp.partial_dependence_plots( clf, X_train.values, features=list(cont_ranked), grid_resolution=18, kind="average", target_class=1, ) fig = disp.figure_ fig.set_size_inches(11, 7) for ax, fi in zip(np.ravel(disp.axes_), cont_ranked): ax.set_xlabel(feature_names[fi]) ax.set_ylabel("Predicted P(disease)") fig.suptitle( "How average predicted risk moves with each measurement\n" "More blocked vessels and deeper ST depression raise risk; a higher peak heart " "rate (better exercise capacity) is protective.", fontsize=11, ) plt.show() ``` ```console theme={null} Partial dependence for: ['Blocked vessels', 'Max heart rate', 'ST depression', 'Cholesterol'] ``` ![Partial Dependence](https://raw.githubusercontent.com/PriorLabs/tabpfn-cookbook/main/visuals/interpret_results/plot-02.png) ## Explaining Individual Predictions *Waterfall explanations for specific patients.* Global views aside, clinicians reason about individuals. Starting from the population base rate, a waterfall plot shows exactly which features moved a single patient's predicted risk up or down. We look at the highest-risk, lowest-risk, and a borderline patient. ```python theme={null} # @title p = sv.values.sum(1) + sv.base_values idx_high = int(np.argmax(p)) # Highest risk patient idx_low = int(np.argmin(p)) # Lowest risk patient idx_borderline = int(np.argmin(np.abs(p - 0.5))) base_rate = float(sv.base_values[0]) print(f"Every explanation starts from the population base rate of {base_rate:.0%}.\n") def show_value(name, v): if name in DECODE: return DECODE[name].get(int(v), str(v)) return f"{v:g}" def describe(idx, label): row = sv[idx] vals = X_test.iloc[idx] row.display_data = np.array( [show_value(feature_names[j], vals.iloc[j]) for j in range(len(feature_names))], dtype=object, ) print(f"[{label}] predicted risk = {p[idx]:.0%} (base rate {base_rate:.0%})") for j in np.argsort(-np.abs(row.values))[:5]: name = feature_names[j] arrow = "raises" if row.values[j] > 0 else "lowers" print( f" {name:<18} = {show_value(name, vals.iloc[j]):<18} {arrow} risk by " f"{abs(row.values[j]) * 100:4.1f} points" ) print() shap.plots.waterfall(row, show=False) fig = plt.gcf() fig.set_size_inches(9, 5) fig.suptitle(f"{label}: predicted coronary-disease risk {p[idx]:.0%}", fontsize=11) return fig ``` ```console theme={null} Every explanation starts from the population base rate of 53%. ``` ```python theme={null} fig = describe(idx_high, "Highest-risk patient") plt.show() ``` ```console theme={null} [Highest-risk patient] predicted risk = 100% (base rate 53%) Blocked vessels = 3 raises risk by 16.1 points Chest pain type = asymptomatic raises risk by 8.1 points ST slope = flat raises risk by 6.2 points Thallium scan = reversible defect raises risk by 6.1 points Exercise angina = yes raises risk by 4.7 points ``` ![Explaining Individual Predictions](https://raw.githubusercontent.com/PriorLabs/tabpfn-cookbook/main/visuals/interpret_results/plot-03.png) ```python theme={null} fig = describe(idx_low, "Lowest-risk patient") plt.show() ``` ```console theme={null} [Lowest-risk patient] predicted risk = 1% (base rate 53%) Blocked vessels = 0 lowers risk by 9.8 points Chest pain type = non-anginal lowers risk by 8.9 points ST slope = upsloping lowers risk by 7.8 points Sex = female lowers risk by 7.7 points Exercise angina = no lowers risk by 4.3 points ``` ![Explaining Individual Predictions](https://raw.githubusercontent.com/PriorLabs/tabpfn-cookbook/main/visuals/interpret_results/plot-04.png) ```python theme={null} fig = describe(idx_borderline, "Borderline patient") plt.show() ``` ```console theme={null} [Borderline patient] predicted risk = 47% (base rate 53%) Blocked vessels = 0 lowers risk by 15.3 points Thallium scan = reversible defect raises risk by 14.9 points Chest pain type = typical angina lowers risk by 13.2 points ST slope = flat raises risk by 7.2 points Sex = male raises risk by 5.5 points ``` ![Explaining Individual Predictions](https://raw.githubusercontent.com/PriorLabs/tabpfn-cookbook/main/visuals/interpret_results/plot-05.png)