---
annotations_creators: []
license: []
pretty_name: tabular_benchmark
tags: []
task_categories:
- tabular-classification
- tabular-regression
dataset_info:
splits:
- name: reg_num
- name: reg_cat
- name: clf_num
- name: clf_cat
---
# Dataset Card for Tabular Benchmark
## Dataset Description
- **Repository:** https://github.com/LeoGrin/tabular-benchmark/community
- **Paper:** https://hal.archives-ouvertes.fr/hal-03723551v2/document
### Dataset Summary
Benchmark made of curation of various tabular data learning tasks, including:
- Regression from Numerical and Categorical Features
- Regression from Numerical Features
- Classification from Numerical and Categorical Features
- Classification from Numerical Features
### Supported Tasks and Leaderboards
- `tabular-regression`
- `tabular-classification`
## Dataset Structure
### Data Splits
This dataset consists of four splits (folders) based on tasks and datasets included in tasks.
- reg_num: Task identifier for regression on numerical features.
- reg_cat: Task identifier for regression on numerical and categorical features.
- clf_num: Task identifier for classification on numerical features.
- clf_cat: Task identifier for classification on categorical features.
Depending on the dataset you want to load, you can load the dataset by passing `task_name/dataset_name` to `data_file` argument of `load_dataset` like below:
```python
from datasets import load_dataset
dataset = load_dataset("inria_soda/tabular-benchmark", data_file="reg_cat/house_sales.csv")
```
## Dataset Creation
### Curation Rationale
- Heterogeneous columns. Columns should correspond to features of different nature. This excludes
images or signal datasets where each column corresponds to the same signal on different sensors.
- Not high dimensional. We only keep datasets with a d/n ratio below 1/10.
- Undocumented datasets We remove datasets where too little information is available. We did keep
datasets with hidden column names if it was clear that the features were heterogeneous.
- I.I.D. data. We remove stream-like datasets or time series.
Real-world data. We remove artificial datasets but keep some simulated datasets. The difference is
subtle, but we try to keep simulated datasets if learning these datasets are of practical importance
(like the Higgs dataset), and not just a toy example to test specific model capabilities.
Not too small. We remove datasets with too few features (< 4) and too few samples (< 3 000). For
benchmarks on numerical features only, we remove categorical features before checking if enough
features and samples are remaining.
- Not too easy. We remove datasets which are too easy. Specifically, we remove a dataset if a default
Logistic Regression (or Linear Regression for regression) reach a score whose relative difference
with the score of both a default Resnet (from Gorishniy et al. [2021]) and a default HistGradientBoosting model (from scikit learn) is below 5%. Other benchmarks use different metrics to
remove too easy datasets, like removing datasets which can be learnt perfectly by a single decision
classifier [Bischl et al., 2021], but this does not account for different Bayes rate of different datasets.
As tree-based methods have been shown to be superior to Logistic Regression [Fernández-Delgado
et al., 2014] in our setting, a close score for these two types of models indicates that we might
already be close to the best achievable score.
- Not deterministic. We remove datasets where the target is a deterministic function of the data. This
mostly means removing datasets on games like poker and chess. Indeed, we believe that these
datasets are very different from most real-world tabular datasets, and should be studied separately
### Source Data
Numerical Classification
dataset_name n_samples n_features original_link new_link
credit 16714 10 https://openml.org/d/151 https://www.openml.org/d/44089
california 20634 8 https://openml.org/d/293 https://www.openml.org/d/44090
wine 2554 11 https://openml.org/d/722 https://www.openml.org/d/44091
electricity 38474 7 https://openml.org/d/821 https://www.openml.org/d/44120
covertype 566602 10 https://openml.org/d/993 https://www.openml.org/d/44121
pol 10082 26 https://openml.org/d/1120 https://www.openml.org/d/44122
house_16H 13488 16 https://openml.org/d/1461 https://www.openml.org/d/44123
kdd_ipums_la_97-small 5188 20 https://openml.org/d/1489 https://www.openml.org/d/44124
MagicTelescope 13376 10 https://openml.org/d/41150 https://www.openml.org/d/44125
bank-marketing 10578 7 https://openml.org/d/42769 https://www.openml.org/d/44126
phoneme 3172 5 https://openml.org/d/1044 https://www.openml.org/d/44127
MiniBooNE 72998 50 https://openml.org/d/41168 https://www.openml.org/d/44128
Higgs 940160 24 https://www.kaggle.com/c/GiveMeSomeCredit/data?select=cs-training.csv https://www.openml.org/d/44129
eye_movements 7608 20 https://www.dcc.fc.up.pt/ ltorgo/Regression/cal_housing.html https://www.openml.org/d/44130
jannis 57580 54 https://archive.ics.uci.edu/ml/datasets/wine+quality https://www.openml.org/d/44131
Note that we noticed a bit late that the number of samples in the transfo
Categorical Classification
dataset_name n_samples n_features original_link new_link
electricity 38474 8 https://openml.org/d/151 https://www.openml.org/d/44156
eye_movements 7608 23 https://openml.org/d/1044 https://www.openml.org/d/44157
covertype 423680 54 https://openml.org/d/1114 https://www.openml.org/d/44159
rl 4970 12 https://openml.org/d/1596 https://www.openml.org/d/44160
road-safety 111762 32 https://openml.org/d/41160 https://www.openml.org/d/44161
compass 16644 17 https://openml.org/d/42803 https://www.openml.org/d/44162
KDDCup09_upselling 5128 49 https://www.kaggle.com/datasets/danofer/compass?select=cox-violent-parsed.csv https://www.openml.org/d/44186
Numerical Regression
dataset_name n_samples n_features original link new_link
cpu_act 8192 21 https://openml.org/d/197 https://www.openml.org/d/44132
pol 15000 26 https://openml.org/d/201 https://www.openml.org/d/44133
elevators 16599 16 https://openml.org/d/216 https://www.openml.org/d/44134
isolet 7797 613 https://openml.org/d/300 https://www.openml.org/d/44135
wine_quality 6497 11 https://openml.org/d/287 https://www.openml.org/d/44136
Ailerons 13750 33 https://openml.org/d/296 https://www.openml.org/d/44137
houses 20640 8 https://openml.org/d/537 https://www.openml.org/d/44138
house_16H 22784 16 https://openml.org/d/574 https://www.openml.org/d/44139
diamonds 53940 6 https://openml.org/d/42225 https://www.openml.org/d/44140
Brazilian_houses 10692 8 https://openml.org/d/42688 https://www.openml.org/d/44141
Bike_Sharing_Demand 17379 6 https://openml.org/d/42712 https://www.openml.org/d/44142
nyc-taxi-green-dec-2016 581835 9 https://openml.org/d/42729 https://www.openml.org/d/44143
house_sales 21613 15 https://openml.org/d/42731 https://www.openml.org/d/44144
sulfur 10081 6 https://openml.org/d/23515 https://www.openml.org/d/44145
medical_charges 163065 3 https://openml.org/d/42720 https://www.openml.org/d/44146
MiamiHousing2016 13932 13 https://openml.org/d/43093 https://www.openml.org/d/44147
superconduct 21263 79 https://openml.org/d/43174 https://www.openml.org/d/44148
california 20640 8 https://www.dcc.fc.up.pt/ ltorgo/Regression/cal_housing.html https://www.openml.org/d/44025
fifa 18063 5 https://www.kaggle.com/datasets/stefanoleone992/fifa-22-complete-player-dataset https://www.openml.org/d/44026
year 515345 90 https://archive.ics.uci.edu/ml/datasets/yearpredictionmsd https://www.openml.org/d/44027
Categorical Regression
yprop_4_1 8885 62 https://openml.org/d/416 https://www.openml.org/d/44054
analcatdata_supreme 4052 7 https://openml.org/d/504 https://www.openml.org/d/44055
visualizing_soil 8641 4 https://openml.org/d/688 https://www.openml.org/d/44056
black_friday 166821 9 https://openml.org/d/41540 https://www.openml.org/d/44057
diamonds 53940 9 https://openml.org/d/42225 https://www.openml.org/d/44059
Mercedes_Benz_Greener_Manufacturing 4209 359 https://openml.org/d/42570 https://www.openml.org/d/44061
Brazilian_houses 10692 11 https://openml.org/d/42688 https://www.openml.org/d/44062
Bike_Sharing_Demand 17379 11 https://openml.org/d/42712 https://www.openml.org/d/44063
OnlineNewsPopularity 39644 59 https://openml.org/d/42724 https://www.openml.org/d/44064
nyc-taxi-green-dec-2016 581835 16 https://openml.org/d/42729 https://www.openml.org/d/44065
house_sales 21613 17 https://openml.org/d/42731 https://www.openml.org/d/44066
particulate-matter-ukair-2017 394299 6 https://openml.org/d/42207 https://www.openml.org/d/44068
SGEMM_GPU_kernel_performance 241600 9 https://openml.org/d/43144 https://www.openml.org/d/44069
#### Initial Data Collection and Normalization
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
## Additional Information
### Dataset Curators
Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux.
### Licensing Information
[More Information Needed]
### Citation Information
Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux. Why do tree-based models still outperform deep
learning on typical tabular data?. NeurIPS 2022 Datasets and Benchmarks Track, Nov 2022, New
Orleans, United States. ffhal-03723551v2f