update-datasets

.gitattributes

@@ -65,3 +65,8 @@ reg_cat/nyc-taxi-green-dec-2016.csv filter=lfs diff=lfs merge=lfs -text
 reg_cat/particulate-matter-ukair-2017.csv filter=lfs diff=lfs merge=lfs -text
 clf_cat/road-safety.csv filter=lfs diff=lfs merge=lfs -text
 clf_cat/covertype.csv filter=lfs diff=lfs merge=lfs -text
+reg_num/delays_zurich_transport.csv filter=lfs diff=lfs merge=lfs -text
+reg_cat/Allstate_Claims_Severity.csv filter=lfs diff=lfs merge=lfs -text
+reg_cat/Airlines_DepDelay_1M.csv filter=lfs diff=lfs merge=lfs -text
+reg_cat/delays_zurich_transport.csv filter=lfs diff=lfs merge=lfs -text
+reg_cat/topo_2_1.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -81,14 +81,11 @@ subtle, but we try to keep simulated datasets if learning these datasets are of
 - **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 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
@@ -97,79 +94,81 @@ datasets are very different from most real-world tabular datasets, and should be
 
 
 **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|
+|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| 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 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|
-|----|----|----|----|----|
-|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_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
@@ -184,4 +183,4 @@ Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux.
 
 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
+Orleans, United States. ffhal-03723551v2f

clf_cat/covertype.csv

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clf_cat/road-safety.csv

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clf_num/Higgs.csv

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clf_num/MiniBooNE.csv

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clf_num/jannis.csv

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reg_cat/{OnlineNewsPopularity.csv → Airlines_DepDelay_1M.csv}

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reg_num/isolet.csv → reg_cat/Allstate_Claims_Severity.csv

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reg_cat/SGEMM_GPU_kernel_performance.csv

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reg_num/year.csv → reg_cat/delays_zurich_transport.csv

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reg_cat/nyc-taxi-green-dec-2016.csv

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reg_cat/particulate-matter-ukair-2017.csv

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reg_cat/topo_2_1.csv

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