File size: 14,997 Bytes
6e48f4f 3d146c2 8d0ff91 6e48f4f f918b61 6e48f4f b42b537 6e48f4f 8050af3 6e48f4f 02c346e 6e48f4f b42b537 7c221f6 6e48f4f 7c221f6 6e48f4f 7c221f6 6e48f4f 7c221f6 6e48f4f dabc0f5 7c221f6 6e48f4f b42b537 dabc0f5 b42b537 6e48f4f dabc0f5 6e48f4f dabc0f5 6e48f4f 7944cfd dabc0f5 6e48f4f dabc0f5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 |
---
annotations_creators: []
license: []
pretty_name: tabular_benchmark
tags: []
task_categories:
- tabular-classification
- tabular-regression
configs:
- config_name: clf_cat_albert
data_files: clf_cat/albert.csv
- config_name: clf_cat_compas-two-years
data_files: clf_cat/compas-two-years.csv
- config_name: clf_cat_covertype
data_files: clf_cat/covertype.csv
- config_name: clf_cat_default-of-credit-card-clients
data_files: clf_cat/default-of-credit-card-clients.csv
- config_name: clf_cat_electricity
data_files: clf_cat/electricity.csv
- config_name: clf_cat_eye_movements
data_files: clf_cat/eye_movements.csv
- config_name: clf_cat_road-safety
data_files: clf_cat/road-safety.csv
- config_name: clf_num_Bioresponse
data_files: clf_num/Bioresponse.csv
- config_name: clf_num_Diabetes130US
data_files: clf_num/Diabetes130US.csv
- config_name: clf_num_Higgs
data_files: clf_num/Higgs.csv
- config_name: clf_num_MagicTelescope
data_files: clf_num/MagicTelescope.csv
- config_name: clf_num_MiniBooNE
data_files: clf_num/MiniBooNE.csv
- config_name: clf_num_bank-marketing
data_files: clf_num/bank-marketing.csv
- config_name: clf_num_california
data_files: clf_num/california.csv
- config_name: clf_num_covertype
data_files: clf_num/covertype.csv
- config_name: clf_num_credit
data_files: clf_num/credit.csv
- config_name: clf_num_default-of-credit-card-clients
data_files: clf_num/default-of-credit-card-clients.csv
- config_name: clf_num_electricity
data_files: clf_num/electricity.csv
- config_name: clf_num_eye_movements
data_files: clf_num/eye_movements.csv
- config_name: clf_num_heloc
data_files: clf_num/heloc.csv
- config_name: clf_num_house_16H
data_files: clf_num/house_16H.csv
- config_name: clf_num_jannis
data_files: clf_num/jannis.csv
- config_name: clf_num_pol
data_files: clf_num/pol.csv
- config_name: reg_cat_Airlines_DepDelay_1M
data_files: reg_cat/Airlines_DepDelay_1M.csv
- config_name: reg_cat_Allstate_Claims_Severity
data_files: reg_cat/Allstate_Claims_Severity.csv
- config_name: reg_cat_Bike_Sharing_Demand
data_files: reg_cat/Bike_Sharing_Demand.csv
- config_name: reg_cat_Brazilian_houses
data_files: reg_cat/Brazilian_houses.csv
- config_name: reg_cat_Mercedes_Benz_Greener_Manufacturing
data_files: reg_cat/Mercedes_Benz_Greener_Manufacturing.csv
- config_name: reg_cat_SGEMM_GPU_kernel_performance
data_files: reg_cat/SGEMM_GPU_kernel_performance.csv
- config_name: reg_cat_abalone
data_files: reg_cat/abalone.csv
- config_name: reg_cat_analcatdata_supreme
data_files: reg_cat/analcatdata_supreme.csv
- config_name: reg_cat_delays_zurich_transport
data_files: reg_cat/delays_zurich_transport.csv
- config_name: reg_cat_diamonds
data_files: reg_cat/diamonds.csv
- config_name: reg_cat_house_sales
data_files: reg_cat/house_sales.csv
- config_name: reg_cat_medical_charges
data_files: reg_cat/medical_charges.csv
- config_name: reg_cat_nyc-taxi-green-dec-2016
data_files: reg_cat/nyc-taxi-green-dec-2016.csv
- config_name: reg_cat_particulate-matter-ukair-2017
data_files: reg_cat/particulate-matter-ukair-2017.csv
- config_name: reg_cat_seattlecrime6
data_files: reg_cat/seattlecrime6.csv
- config_name: reg_cat_topo_2_1
data_files: reg_cat/topo_2_1.csv
- config_name: reg_cat_visualizing_soil
data_files: reg_cat/visualizing_soil.csv
- config_name: reg_num_Ailerons
data_files: reg_num/Ailerons.csv
- config_name: reg_num_Bike_Sharing_Demand
data_files: reg_num/Bike_Sharing_Demand.csv
- config_name: reg_num_Brazilian_houses
data_files: reg_num/Brazilian_houses.csv
- config_name: reg_num_MiamiHousing2016
data_files: reg_num/MiamiHousing2016.csv
- config_name: reg_num_abalone
data_files: reg_num/abalone.csv
- config_name: reg_num_cpu_act
data_files: reg_num/cpu_act.csv
- config_name: reg_num_delays_zurich_transport
data_files: reg_num/delays_zurich_transport.csv
- config_name: reg_num_diamonds
data_files: reg_num/diamonds.csv
- config_name: reg_num_elevators
data_files: reg_num/elevators.csv
- config_name: reg_num_house_16H
data_files: reg_num/house_16H.csv
- config_name: reg_num_house_sales
data_files: reg_num/house_sales.csv
- config_name: reg_num_houses
data_files: reg_num/houses.csv
- config_name: reg_num_medical_charges
data_files: reg_num/medical_charges.csv
- config_name: reg_num_nyc-taxi-green-dec-2016
data_files: reg_num/nyc-taxi-green-dec-2016.csv
- config_name: reg_num_pol
data_files: reg_num/pol.csv
- config_name: reg_num_sulfur
data_files: reg_num/sulfur.csv
- config_name: reg_num_superconduct
data_files: reg_num/superconduct.csv
- config_name: reg_num_wine_quality
data_files: reg_num/wine_quality.csv
- config_name: reg_num_yprop_4_1
data_files: reg_num/yprop_4_1.csv
---
# Tabular Benchmark
## Dataset Description
This dataset is a curation of various datasets from [openML](https://www.openml.org/) and is curated to benchmark performance of various machine learning algorithms.
- **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_files` argument of `load_dataset` like below:
```python
from datasets import load_dataset
dataset = load_dataset("inria-soda/tabular-benchmark", data_files="reg_cat/house_sales.csv")
```
## Dataset Creation
### Curation Rationale
This dataset is curated to benchmark performance of tree based models against neural networks. The process of picking the datasets for curation is mentioned in the paper as below:
- **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 simple model (max of a single tree and a regression, logistic or OLS)
reaches 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 perfectly separated by a single decision classifier [Bischl et al., 2021],
but this ignores varying Bayes rate across datasets. As tree ensembles are superior to simple trees and logistic regresison [Fernández-Delgado et al., 2014],
a close score for the simple and powerful models suggests that we are already 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|
|---|---|---|---|---|
|electricity|38474.0|7.0|https://www.openml.org/d/151|https://www.openml.org/d/44120|
|covertype|566602.0|10.0|https://www.openml.org/d/293|https://www.openml.org/d/44121|
|pol|10082.0|26.0|https://www.openml.org/d/722|https://www.openml.org/d/44122|
|house_16H|13488.0|16.0|https://www.openml.org/d/821|https://www.openml.org/d/44123|
|MagicTelescope|13376.0|10.0|https://www.openml.org/d/1120|https://www.openml.org/d/44125|
|bank-marketing|10578.0|7.0|https://www.openml.org/d/1461|https://www.openml.org/d/44126|
|Bioresponse|3434.0|419.0|https://www.openml.org/d/4134|https://www.openml.org/d/45019|
|MiniBooNE|72998.0|50.0|https://www.openml.org/d/41150|https://www.openml.org/d/44128|
|default-of-credit-card-clients|13272.0|20.0|https://www.openml.org/d/42477|https://www.openml.org/d/45020|
|Higgs|940160.0|24.0|https://www.openml.org/d/42769|https://www.openml.org/d/44129|
|eye_movements|7608.0|20.0|https://www.openml.org/d/1044|https://www.openml.org/d/44130|
|Diabetes130US|71090.0|7.0|https://www.openml.org/d/4541|https://www.openml.org/d/45022|
|jannis|57580.0|54.0|https://www.openml.org/d/41168|https://www.openml.org/d/45021|
|heloc|10000.0|22.0|"https://www.kaggle.com/datasets/averkiyoliabev/home-equity-line-of-creditheloc?select=heloc_dataset_v1+%281%29.csv"|https://www.openml.org/d/45026|
|credit|16714.0|10.0|"https://www.kaggle.com/c/GiveMeSomeCredit/data?select=cs-training.csv"|https://www.openml.org/d/44089|
|california|20634.0|8.0|"https://www.dcc.fc.up.pt/ltorgo/Regression/cal_housing.html"|https://www.openml.org/d/45028|
**Categorical Classification**
|dataset_name|n_samples|n_features|original_link|new_link|
|---|---|---|---|---|
|electricity|38474.0|8.0|https://www.openml.org/d/151|https://www.openml.org/d/44156|
|eye_movements|7608.0|23.0|https://www.openml.org/d/1044|https://www.openml.org/d/44157|
|covertype|423680.0|54.0|https://www.openml.org/d/1596|https://www.openml.org/d/44159|
|albert|58252.0|31.0|https://www.openml.org/d/41147|https://www.openml.org/d/45035|
|compas-two-years|4966.0|11.0|https://www.openml.org/d/42192|https://www.openml.org/d/45039|
|default-of-credit-card-clients|13272.0|21.0|https://www.openml.org/d/42477|https://www.openml.org/d/45036|
|road-safety|111762.0|32.0|https://www.openml.org/d/42803|https://www.openml.org/d/45038|
**Numerical Regression**
|dataset_name|n_samples|n_features|original_link|new_link|
|---|---|---|---|---|
|cpu_act|8192.0|21.0|https://www.openml.org/d/197|https://www.openml.org/d/44132|
|pol|15000.0|26.0|https://www.openml.org/d/201|https://www.openml.org/d/44133|
|elevators|16599.0|16.0|https://www.openml.org/d/216|https://www.openml.org/d/44134|
|wine_quality|6497.0|11.0|https://www.openml.org/d/287|https://www.openml.org/d/44136|
|Ailerons|13750.0|33.0|https://www.openml.org/d/296|https://www.openml.org/d/44137|
|yprop_4_1|8885.0|42.0|https://www.openml.org/d/416|https://www.openml.org/d/45032|
|houses|20640.0|8.0|https://www.openml.org/d/537|https://www.openml.org/d/44138|
|house_16H|22784.0|16.0|https://www.openml.org/d/574|https://www.openml.org/d/44139|
|delays_zurich_transport|5465575.0|9.0|https://www.openml.org/d/40753|https://www.openml.org/d/45034|
|diamonds|53940.0|6.0|https://www.openml.org/d/42225|https://www.openml.org/d/44140|
|Brazilian_houses|10692.0|8.0|https://www.openml.org/d/42688|https://www.openml.org/d/44141|
|Bike_Sharing_Demand|17379.0|6.0|https://www.openml.org/d/42712|https://www.openml.org/d/44142|
|nyc-taxi-green-dec-2016|581835.0|9.0|https://www.openml.org/d/42729|https://www.openml.org/d/44143|
|house_sales|21613.0|15.0|https://www.openml.org/d/42731|https://www.openml.org/d/44144|
|sulfur|10081.0|6.0|https://www.openml.org/d/23515|https://www.openml.org/d/44145|
|medical_charges|163065.0|5.0|https://www.openml.org/d/42720|https://www.openml.org/d/44146|
|MiamiHousing2016|13932.0|14.0|https://www.openml.org/d/43093|https://www.openml.org/d/44147|
|superconduct|21263.0|79.0|https://www.openml.org/d/43174|https://www.openml.org/d/44148|
**Categorical Regression**
|dataset_name|n_samples|n_features|original_link|new_link|
|---|---|---|---|---|
|topo_2_1|8885.0|255.0|https://www.openml.org/d/422|https://www.openml.org/d/45041|
|analcatdata_supreme|4052.0|7.0|https://www.openml.org/d/504|https://www.openml.org/d/44055|
|visualizing_soil|8641.0|4.0|https://www.openml.org/d/688|https://www.openml.org/d/44056|
|delays_zurich_transport|5465575.0|12.0|https://www.openml.org/d/40753|https://www.openml.org/d/45045|
|diamonds|53940.0|9.0|https://www.openml.org/d/42225|https://www.openml.org/d/44059|
|Allstate_Claims_Severity|188318.0|124.0|https://www.openml.org/d/42571|https://www.openml.org/d/45046|
|Mercedes_Benz_Greener_Manufacturing|4209.0|359.0|https://www.openml.org/d/42570|https://www.openml.org/d/44061|
|Brazilian_houses|10692.0|11.0|https://www.openml.org/d/42688|https://www.openml.org/d/44062|
|Bike_Sharing_Demand|17379.0|11.0|https://www.openml.org/d/42712|https://www.openml.org/d/44063|
|Airlines_DepDelay_1M|1000000.0|5.0|https://www.openml.org/d/42721|https://www.openml.org/d/45047|
|nyc-taxi-green-dec-2016|581835.0|16.0|https://www.openml.org/d/42729|https://www.openml.org/d/44065|
|abalone|4177.0|8.0|https://www.openml.org/d/42726|https://www.openml.org/d/45042|
|house_sales|21613.0|17.0|https://www.openml.org/d/42731|https://www.openml.org/d/44066|
|seattlecrime6|52031.0|4.0|https://www.openml.org/d/42496|https://www.openml.org/d/45043|
|medical_charges|163065.0|5.0|https://www.openml.org/d/42720|https://www.openml.org/d/45048|
|particulate-matter-ukair-2017|394299.0|6.0|https://www.openml.org/d/42207|https://www.openml.org/d/44068|
|SGEMM_GPU_kernel_performance|241600.0|9.0|https://www.openml.org/d/43144|https://www.openml.org/d/44069|
### 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
|