Model interpretability with TabICL

TabICL comes with a fast approximations of SHAP values. It is much faster than using black-box shape routines on TabICL which is slow.

Here we demo it on dataset on wages

The dataset: wages

from sklearn.datasets import fetch_openml

survey = fetch_openml(data_id=534, as_frame=True)

X = survey.data[survey.feature_names]

A quick glance at the data with skrub’s TableReport

import skrub

skrub.TableReport(X)

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We need to convert the categorical features to numeric ones. We can do this with pandas’ get_dummies

import pandas as pd

X = pd.get_dummies(X, drop_first=True)

The values to predict: wages

y = survey.target.values.ravel()

Split out a test set

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y)

Our TabICL model

from tabicl import TabICLRegressor

clf = TabICLRegressor(n_estimators=4, device="cpu")
clf.fit(X_train, y_train)

Checkpoint 'tabicl-regressor-v2-20260212.ckpt' not cached.
 Downloading from Hugging Face Hub (jingang/TabICL).

TabICLRegressor(device='cpu', n_estimators=4)

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Shap-like interpretability

Use TabICL’s fast approximations of shap-like values and plot them

This part of the example requires to install the shap extra: pip install ‘tabicl[shap]

from tabicl.shap import get_shap_values, plot_shap

# Compute the shap values
sv = get_shap_values(clf, X_test[:10])

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Bar plot of mean absolute SHAP values, showing aggregate feature importances

plot_shap(sv, kind="bar")

Beeswarm plot showing per-sample SHAP values for each feature

plot_shap(sv, kind="beeswarm")

Note that these are approximate SHAP values, and not exact ones.