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README.md
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## Dataset Description
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- **Repository:** https://github.com/LeoGrin/tabular-benchmark/community
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- **Paper:** https://hal.archives-ouvertes.fr/hal-03723551v2/document
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## Dataset Creation
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### Curation Rationale
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- Heterogeneous columns. Columns should correspond to features of different nature. This excludes
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images or signal datasets where each column corresponds to the same signal on different sensors.
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- Not high dimensional. We only keep datasets with a d/n ratio below 1/10.
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### Source Data
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Numerical Classification
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Categorical Classification
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dataset_name n_samples n_features original_link new_link
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Numerical Regression
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Categorical Regression
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#### Initial Data Collection and Normalization
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[More Information Needed]
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### Annotations
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#### Annotation process
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[More Information Needed]
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#### Who are the annotators?
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[More Information Needed]
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## Additional Information
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### Dataset Curators
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## Dataset Description
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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.
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- **Repository:** https://github.com/LeoGrin/tabular-benchmark/community
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- **Paper:** https://hal.archives-ouvertes.fr/hal-03723551v2/document
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```
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## Dataset Creation
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### Curation Rationale
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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:
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- Heterogeneous columns. Columns should correspond to features of different nature. This excludes
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images or signal datasets where each column corresponds to the same signal on different sensors.
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- Not high dimensional. We only keep datasets with a d/n ratio below 1/10.
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### Source Data
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**Numerical Classification**
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|dataset_name| n_samples| n_features| original_link| new_link|
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|credit| 16714| 10 |https://openml.org/d/151 |https://www.openml.org/d/44089|
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|california |20634 |8 |https://openml.org/d/293 |https://www.openml.org/d/44090|
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|wine |2554 |11 |https://openml.org/d/722 |https://www.openml.org/d/44091|
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|electricity| 38474 |7| https://openml.org/d/821 |https://www.openml.org/d/44120|
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|covertype |566602 |10 |https://openml.org/d/993| https://www.openml.org/d/44121|
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|pol |10082 |26 |https://openml.org/d/1120 |https://www.openml.org/d/44122|
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|house_16H |13488| 16 |https://openml.org/d/1461| https://www.openml.org/d/44123|
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|kdd_ipums_la_97-small| 5188 |20 |https://openml.org/d/1489 |https://www.openml.org/d/44124|
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|MagicTelescope| 13376| 10| https://openml.org/d/41150 |https://www.openml.org/d/44125|
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|bank-marketing |10578 |7 |https://openml.org/d/42769| https://www.openml.org/d/44126|
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|phoneme |3172| 5 |https://openml.org/d/1044| https://www.openml.org/d/44127|
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|MiniBooNE| 72998| 50 |https://openml.org/d/41168 |https://www.openml.org/d/44128|
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|Higgs| 940160 |24| https://www.kaggle.com/c/GiveMeSomeCredit/data?select=cs-training.csv |https://www.openml.org/d/44129|
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|eye_movements| 7608 |20 |https://www.dcc.fc.up.pt/ltorgo/Regression/cal_housing.html |https://www.openml.org/d/44130|
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|jannis |57580 |54 |https://archive.ics.uci.edu/ml/datasets/wine+quality |https://www.openml.org/d/44131|
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**Categorical Classification**
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|dataset_name |n_samples| n_features |original_link |new_link|
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|electricity |38474| 8 |https://openml.org/d/151| https://www.openml.org/d/44156|
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|eye_movements |7608 |23| https://openml.org/d/1044 |https://www.openml.org/d/44157|
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|covertype |423680| 54| https://openml.org/d/1114 |https://www.openml.org/d/44159|
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|rl |4970 |12 |https://openml.org/d/1596 |https://www.openml.org/d/44160|
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|road-safety| 111762 |32 |https://openml.org/d/41160 |https://www.openml.org/d/44161|
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|compass |16644 |17 |https://openml.org/d/42803 |https://www.openml.org/d/44162|
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|KDDCup09_upselling |5128 |49 |https://www.kaggle.com/datasets/danofer/compass?select=cox-violent-parsed.csv |https://www.openml.org/d/44186|
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Numerical Regression
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|dataset_name| n_samples| n_features| original_link| new_link|
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|cpu_act |8192 |21| https://openml.org/d/197 |https://www.openml.org/d/44132|
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|pol | 15000| 26 |https://openml.org/d/201| https://www.openml.org/d/44133|
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|elevators |16599 |16 |https://openml.org/d/216| https://www.openml.org/d/44134|
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|isolet |7797| 613| https://openml.org/d/300| https://www.openml.org/d/44135|
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|wine_quality |6497 |11| https://openml.org/d/287 | https://www.openml.org/d/44136|
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|Ailerons |13750 |33| https://openml.org/d/296 | https://www.openml.org/d/44137|
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|houses |20640| 8| https://openml.org/d/537 | https://www.openml.org/d/44138|
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|house_16H |22784| 16 |https://openml.org/d/574 | https://www.openml.org/d/44139|
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|diamonds |53940| 6| https://openml.org/d/42225 | https://www.openml.org/d/44140|
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|Brazilian_houses |10692| 8 |https://openml.org/d/42688 | https://www.openml.org/d/44141|
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|Bike_Sharing_Demand| 17379| 6| https://openml.org/d/42712 | https://www.openml.org/d/44142|
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|nyc-taxi-green-dec-2016 |581835| 9| https://openml.org/d/42729 | https://www.openml.org/d/44143|
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|house_sales |21613 |15 | https://openml.org/d/42731| https://www.openml.org/d/44144|
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|sulfur |10081| 6 | https://openml.org/d/23515 | https://www.openml.org/d/44145|
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|medical_charges | 163065 |3 | https://openml.org/d/42720 | https://www.openml.org/d/44146|
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|MiamiHousing2016 |13932| 13 |https://openml.org/d/43093 | https://www.openml.org/d/44147|
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|superconduct |21263 |79| https://openml.org/d/43174 | https://www.openml.org/d/44148|
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|california |20640| 8 |https://www.dcc.fc.up.pt/ ltorgo/Regression/cal_housing.html |https://www.openml.org/d/44025|
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|fifa |18063 |5 |https://www.kaggle.com/datasets/stefanoleone992/fifa-22-complete-player-dataset| https://www.openml.org/d/44026|
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|year |515345 |90 |https://archive.ics.uci.edu/ml/datasets/yearpredictionmsd| https://www.openml.org/d/44027|
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Categorical Regression
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|dataset_name| n_samples| n_features| original_link| new_link|
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|yprop_4_1 |8885 |62 |https://openml.org/d/416 |https://www.openml.org/d/44054|
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|analcatdata_supreme |4052| 7 |https://openml.org/d/504 |https://www.openml.org/d/44055|
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|visualizing_soil |8641| 4 |https://openml.org/d/688 |https://www.openml.org/d/44056|
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|black_friday |166821| 9 |https://openml.org/d/41540| https://www.openml.org/d/44057|
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|diamonds | 53940| 9| https://openml.org/d/42225| https://www.openml.org/d/44059|
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|Mercedes_Benz_Greener_Manufacturing |4209 |359| https://openml.org/d/42570 |https://www.openml.org/d/44061|
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|Brazilian_houses| 10692| 11 |https://openml.org/d/42688 |https://www.openml.org/d/44062|
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|Bike_Sharing_Demand| 17379| 11 |https://openml.org/d/42712 |https://www.openml.org/d/44063|
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|OnlineNewsPopularity |39644| 59| https://openml.org/d/42724| https://www.openml.org/d/44064|
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|nyc-taxi-green-dec-2016| 581835 |16 |https://openml.org/d/42729|https://www.openml.org/d/44065|
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|house_sales | 21613| 17| https://openml.org/d/42731| https://www.openml.org/d/44066|
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|particulate-matter-ukair-2017 |394299 |6| https://openml.org/d/42207| https://www.openml.org/d/44068|
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|SGEMM_GPU_kernel_performance | 241600| 9 |https://openml.org/d/43144| https://www.openml.org/d/44069|
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### Dataset Curators
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