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deup

Tabular regression

ursinasanderink/deup

Tutorial: Tabular regression

Goal: wrap an existing sklearn regressor with DEUP and rank test points by expected error.

Setup

from sklearn.datasets import fetch_california_housing
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

from deup import DEUPRegressor

X, y = fetch_california_housing(return_X_y=True)
X = StandardScaler().fit_transform(X)
X_tr, X_te, y_tr, y_te = train_test_split(X, y, test_size=0.2, random_state=0)

Fit DEUP

model = DEUPRegressor(
    base_model=RandomForestRegressor(n_estimators=80, random_state=0),
    cv=5,
    random_state=0,
)
model.fit(X_tr, y_tr)

Under the hood: OOFErrorCollector gathers out-of-fold squared errors, then ErrorEstimator trains g to predict them.

Predict with uncertainty

pred, unc = model.predict(X_te, return_uncertainty=True)

pred — base model point prediction (refit on all training data)
unc — epistemic estimate g(x) ≥ 0 (higher = less trustworthy)

Optional: tabular preset with density features

from deup.domains.tabular import TabularDEUP

# sklearn default (HistGradientBoosting)
preset = TabularDEUP(cv=5, random_state=0)
preset.fit(X_tr, y_tr)
unc = preset.predict_epistemic(X_te)

LightGBM / XGBoost / CatBoost

# pip install "deup[xgb]"  # or deup[gbm], deup[catboost], deup[gbm-all]
preset = TabularDEUP(backend="xgb", cv=5, random_state=0)
preset.fit(X_tr, y_tr)
unc = preset.predict_epistemic(X_te)

Same OOF + density-feature pipeline; only the base and error models change.

Benchmark context

On California housing, DEUP beats ensemble disagreement and conformal-residual baselines for ranking realized squared error (Spearman ρ ≈ 0.51). See Benchmarks.

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