|
|
| --- |
| annotations_creators: [] |
| license: [] |
| pretty_name: tabular_benchmark |
| tags: [] |
| task_categories: |
| - tabular-classification |
| - tabular-regression |
| configs: |
| - config_name: clf_cat_covertype |
| data_files: clf_cat/covertype.csv |
| - config_name: clf_num_Higgs |
| data_files: clf_num/Higgs.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 |
| |
