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README.md

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-
----
-annotations_creators: []
-license: []
-pretty_name: tabular_benchmark
-tags: []
-task_categories: 
-- tabular-classification
-- tabular-regression
-dataset_info:
-- config_name: reg_cat
-  splits: 
-  - reg_cat/*
-- config_name: reg_num
-  splits: 
-  - reg_num/*
-- config_name: clf_cat
-  splits: 
-  - clf_cat/*
-- config_name: clf_num
-  splits: 
-  - clf_num/*
----
-
-# 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 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|
-
-**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**
-|dataset_name| n_samples| n_features| original_link| new_link|
-|----|----|----|----|----|
-|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|
-
-
-### 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

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