CalArena — Calibration Benchmark Dataset

CalArena is a large-scale benchmark for evaluating post-hoc calibration methods on classification models. It covers 7 benchmarks across tabular and computer vision domains, spanning hundreds of (dataset, model) pairs and three problem types (binary, multiclass and large scale multiclass).

Each entry in the benchmark is a (p_cal, y_cal, p_test, y_test) tuple — the calibration split and test split of predicted probabilities and ground-truth labels for one (dataset, model) pair. Calibration methods are fitted on the calibration split and evaluated on the test split.

This dataset is the data companion to the CalArena code repository.

---

Files

File	Description	Size
tabrepo-binary.h5	Binary classification, classical tabular models	~36 MB
tabrepo-binary-experiments.csv	Experiment index for tabrepo-binary	< 1 MB
tabarena-binary.h5	Binary classification, modern tabular foundation models	~26 MB
tabarena-binary-experiments.csv	Experiment index for tabarena-binary	< 1 MB
cv-binary.h5	Binary classification, computer vision models	< 1 MB
cv-binary-experiments.csv	Experiment index for cv-binary	< 1 MB
tabrepo-multiclass.h5	Multiclass classification, classical tabular models	~115 MB
tabrepo-multiclass-experiments.csv	Experiment index for tabrepo-multiclass	< 1 MB
tabarena-multiclass.h5	Multiclass classification, modern tabular foundation models	~11 MB
tabarena-multiclass-experiments.csv	Experiment index for tabarena-multiclass	< 1 MB
cv-multiclass.h5	Multiclass classification, computer vision models	~39 MB
cv-multiclass-experiments.csv	Experiment index for cv-multiclass	< 1 MB
imagenet-multiclass.h5	1000-class ImageNet, computer vision models	~1.5 GB
imagenet-multiclass-experiments.csv	Experiment index for imagenet-multiclass	< 1 MB

---

Benchmark overview

Benchmark	Problem type	Base models	# Datasets	# Experiments
tabrepo-binary	Binary	8	104 tabular datasets	832
tabarena-binary	Binary	11	30 tabular datasets	314
cv-binary	Binary	9	3 (CIFAR-10†, Breast, Pneumonia)	13
tabrepo-multiclass	Multiclass	8	65 tabular datasets	520
tabarena-multiclass	Multiclass	11	8 tabular datasets	84
cv-multiclass	Multiclass	10	6 (CIFAR-10, CIFAR-100, Birds, SVHN, Derma, OCT)	20
imagenet-multiclass	Large scale multiclass	8	1 (ImageNet)	8

† CIFAR-10 is converted to binary (Animal vs Machine) by marginalising over class groups.

Base models

TabRepo (classical tabular): CatBoost, ExtraTrees, LightGBM, LinearModel, NeuralNetFastAI, NeuralNetTorch, RandomForest, XGBoost. Source: TabRepo repository D244_F3_C1530_200. Best hyperparameter configuration selected per (dataset, model, fold) by validation error.

TabArena (modern tabular): TabPFN-v2.6, TabICLv2, RealTabPFN-v2.5, TabICL_GPU, LimiX_GPU, TabM_GPU, RealMLP_GPU, BetaTabPFN_GPU, ModernNCA_GPU, Mitra_GPU, TabDPT_GPU. Models selected with ≥ 1300 ELO on the TabArena leaderboard (Classification, All Datasets, as of April 1 2026). Source: TabArena.

Computer vision: ResNet, DenseNet, WideResNet, ViT, BEiT, ConvNeXt, Swin, EVA, and others depending on the dataset. Logits sourced from two collections: NN_calibration and Beyond Overconfidence.

---

Data format

HDF5 files

Each .h5 file has the following structure:

{dataset}/
  {model}/
    probas_cal   float32  (n_cal,)           # positive-class probabilities [binary]
                 float32  (n_cal, n_classes) # class probabilities [multiclass]
    labels_cal   int32    (n_cal,)
    probas_test  float32  (n_test,)          # same shape conventions as above
    labels_test  int32    (n_test,)

File-level attributes:

• source — "tabrepo", "tabarena", "cv", or "imagenet"
• problem_type — "binary" or "multiclass"

All probabilities are valid (non-negative, sum to 1 for multiclass). Labels are 0-indexed integers.

Experiment CSV files

Each {benchmark}-experiments.csv lists one row per (dataset, model) pair:

Column	Description
dataset	Dataset name (matches the HDF5 group key)
model	Model name (matches the HDF5 group key)
cal_size	Number of calibration samples
test_size	Number of test samples
n_classes	Number of classes (multiclass benchmarks only)
tabrepo_fold / tabarena_fold	Fold index used (TabRepo/TabArena benchmarks)
tabrepo_config / tabarena_config	Best hyperparameter configuration selected (TabRepo/TabArena)

---

Loading the data

Python (h5py)

import h5py
import numpy as np

with h5py.File("tabrepo-binary.h5", "r") as f:
    # List all (dataset, model) pairs
    pairs = [(ds, mdl) for ds in f for mdl in f[ds]]

    # Load a single experiment
    grp = f["anneal/CatBoost"]
    p_cal   = grp["probas_cal"][:]   # shape (n_cal,)
    y_cal   = grp["labels_cal"][:]   # shape (n_cal,)
    p_test  = grp["probas_test"][:]  # shape (n_test,)
    y_test  = grp["labels_test"][:]  # shape (n_test,)

With the CalArena runner

The CalArena repository provides run_benchmark.py, which loads these files automatically and runs all calibrators:

# Place .h5 and .csv files under calibration_benchmarks/
python run_benchmark.py --benchmark tabrepo-binary

---

Dataset construction

Scripts that were used to generate the benchmarks files can be found in the CalArena repository.

Calibration / test split

For TabRepo and TabArena, the calibration split corresponds to the validation fold of the respective repository, and the test split is the held-out test set. This ensures no data leakage: the base model never sees the calibration set during training.

For computer vision datasets, the calibration and test splits are fixed partitions provided by the original data sources.

Excluded datasets

The following datasets were excluded due to errors in the upstream repositories:

• TabRepo binary: MiniBooNE
• TabRepo multiclass: jannis, kropt, shuttle

---

Intended use

This dataset is intended for:

• Benchmarking post-hoc calibration algorithms on diverse classification tasks
• Studying the relationship between model type, dataset characteristics, and calibration difficulty
• Developing new calibration methods with access to pre-computed probability estimates

---

License

The benchmark data is released under CC BY 4.0. Downstream datasets (OpenML, CIFAR, ImageNet, etc.) retain their original licenses; please consult the respective sources before redistribution.

---

Citation

@inproceedings{calarena2025,
  title     = {CalArena: A Large-Scale Benchmark for Post-Hoc Calibration},
  author    = {...},
  booktitle = {...},
  year      = {2025},
}