Create README.md

README.md

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+
+---
+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