Benchmarking model performance across diverse DataFrameBenchmark datasets.

First install additional dependencies:

pip install optuna
pip install torchmetrics
pip install xgboost
pip install catboost
pip install lightgbm

Then run

# Specify the model from [TabNet, FTTransformer, ResNet, TabTransformer, Trompt
# ExcelFormer, FTTransformerBucket, XGBoost, CatBoost, LightGBM]
model_type=TabNet

# Specify the task type from [binary_classification, regression,
# multiclass_classification]
task_type=binary_classification

# Specify the dataset scale from [small, medium, large]
scale=small

# Specify the dataset idx from [0, 1, ...]
idx=0

# Specify the number of AutoML search trials
num_trials=20

# Specify the path to save the results
result_path=results.pt

# Run hyper-parameter tuning and training of the specified model on a specified
# dataset.
python data_frame_benchmark.py --model_type $model_type\
                               --task_type $task_type\
                               --scale $scale\
                               --idx $idx\
                               --num_trials $num_trials\
                               --result_path $result_path

Leaderboard

We show the current model performance across different datasets. The row denotes the model names and the column denotes the dataset idx. In each cell, we include the mean and standard deviation of the model performance, as well as the total time spent, including Optuna-based hyper-parameter search and final model training.

For the mapping from dataset idx into the actual dataset object, please see the documentation.

task_type: binary_classification

Metric: ROC-AUC, higher the better.

scale: small

Experimental setting: 20 Optuna search trials. 50 epochs of training.

	dataset_0	dataset_1	dataset_2	dataset_3	dataset_4	dataset_5	dataset_6	dataset_7	dataset_8	dataset_9	dataset_10	dataset_11	dataset_12	dataset_13
XGBoost	0.931±0.000 (41s)	1.000±0.000 (4s)	0.935±0.000 (16s)	0.946±0.000 (26s)	0.881±0.000 (10s)	0.951±0.000 (16s)	0.862±0.000 (26s)	0.780±0.000 (11s)	0.983±0.000 (584s)	0.763±0.000 (240s)	0.795±0.000 (11s)	0.950±0.000 (479s)	0.999±0.000 (148s)	0.926±0.000 (3042s)
CatBoost	0.930±0.000 (152s)	1.000±0.000 (9s)	0.938±0.000 (164s)	0.924±0.000 (29s)	0.881±0.000 (27s)	0.963±0.000 (48s)	0.861±0.000 (12s)	0.772±0.000 (10s)	0.930±0.000 (91s)	0.628±0.000 (10s)	0.796±0.000 (15s)	0.948±0.000 (46s)	0.998±0.000 (38s)	0.926±0.000 (115s)
LightGBM	0.931±0.000 (15s)	0.999±0.000 (1s)	0.943±0.000 (23s)	0.943±0.000 (14s)	0.887±0.000 (5s)	0.972±0.000 (11s)	0.862±0.000 (6s)	0.774±0.000 (3s)	0.979±0.000 (41s)	0.732±0.000 (13s)	0.787±0.000 (3s)	0.951±0.000 (13s)	0.999±0.000 (10s)	0.927±0.000 (24s)
Trompt	0.919±0.000 (9627s)	1.000±0.000 (5341s)	0.945±0.000 (14679s)	0.942±0.001 (2752s)	0.881±0.001 (2640s)	0.964±0.001 (5173s)	0.855±0.002 (4249s)	0.778±0.002 (8789s)	0.933±0.001 (9353s)	0.686±0.008 (3105s)	0.793±0.002 (8255s)	0.952±0.001 (4876s)	1.000±0.000 (3558s)	0.916±0.001 (30002s)
ResNet	0.917±0.000 (615s)	1.000±0.000 (71s)	0.937±0.001 (787s)	0.938±0.002 (230s)	0.865±0.001 (183s)	0.960±0.001 (349s)	0.828±0.001 (248s)	0.768±0.002 (205s)	0.925±0.002 (958s)	0.665±0.006 (140s)	0.794±0.002 (76s)	0.946±0.002 (145s)	1.000±0.000 (93s)	0.911±0.001 (880s)
FTTransformerBucket	0.915±0.001 (690s)	0.999±0.001 (354s)	0.936±0.002 (1705s)	0.939±0.002 (484s)	0.876±0.002 (321s)	0.960±0.001 (746s)	0.857±0.000 (549s)	0.771±0.003 (654s)	0.909±0.002 (1177s)	0.636±0.012 (244s)	0.788±0.002 (710s)	0.950±0.001 (510s)	0.999±0.000 (634s)	0.913±0.001 (1164s)
ExcelFormer	0.918±0.001 (1587s)	1.000±0.000 (634s)	0.939±0.001 (1827s)	0.939±0.002 (378s)	0.878±0.003 (251s)	0.969±0.000 (678s)	0.833±0.011 (435s)	0.780±0.002 (938s)	0.921±0.005 (1131s)	0.649±0.008 (519s)	0.794±0.003 (683s)	0.950±0.001 (405s)	0.999±0.000 (1169s)	0.919±0.001 (1798s)
FTTransformer	0.918±0.001 (871s)	1.000±0.000 (571s)	0.940±0.001 (1371s)	0.936±0.001 (458s)	0.874±0.002 (200s)	0.959±0.001 (622s)	0.828±0.001 (339s)	0.773±0.002 (521s)	0.909±0.002 (1488s)	0.635±0.011 (392s)	0.790±0.001 (556s)	0.949±0.002 (374s)	1.000±0.000 (713s)	0.912±0.000 (1855s)
TabNet	0.911±0.001 (150s)	1.000±0.000 (35s)	0.931±0.005 (254s)	0.937±0.003 (125s)	0.864±0.002 (52s)	0.944±0.001 (116s)	0.828±0.001 (79s)	0.771±0.005 (93s)	0.913±0.005 (177s)	0.606±0.014 (65s)	0.790±0.003 (41s)	0.936±0.003 (104s)	1.000±0.000 (64s)	0.910±0.001 (294s)
TabTransformer	0.910±0.001 (2044s)	1.000±0.000 (1321s)	0.928±0.001 (2519s)	0.918±0.003 (134s)	0.829±0.002 (64s)	0.928±0.001 (105s)	0.816±0.002 (99s)	0.757±0.003 (645s)	0.885±0.001 (1167s)	0.652±0.006 (282s)	0.780±0.002 (112s)	0.937±0.001 (117s)	0.996±0.000 (76s)	0.905±0.001 (2283s)

scale: medium

Experimental setting: 20 Optuna search trials for XGBoost and CatBoost. 5 Optuna search trials and 25 epochs training for deep learning models.

	0	1	2	3	4	5	6	7	8
XGBoost	0.597±0.000 (5717s)	0.955±0.000 (468s)	0.653±0.000 (21s)	0.986±0.000 (2020s)	0.722±0.000 (358s)	0.997±0.000 (165577s)	0.878±0.000 (690s)	0.917±0.000 (6431s)	0.808±0.000 (7673s)
CatBoost	0.631±0.000 (1201s)	0.956±0.000 (2963s)	0.649±0.000 (26s)	0.986±0.000 (352s)	0.719±0.000 (244s)	0.987±0.000 (2561s)	0.863±0.000 (212s)	0.896±0.000 (740s)	0.803±0.000 (140s)
LightGBM	0.639±0.000 (49s)	0.955±0.000 (126s)	0.652±0.000 (7s)	0.986±0.000 (99s)	0.723±0.000 (16s)	0.997±0.000 (172s)	0.881±0.000 (83s)	0.914±0.000 (86s)	0.809±0.000 (76s)
Trompt	OOM	0.950±0.000 (28212s)	0.652±0.000 (5962s)	0.982±0.000 (19936s)	0.716±0.000 (7110s)	0.966±0.000 (106916s)	0.882±0.000 (13644s)	0.883±0.000 (17863s)	0.705±0.006 (11563s)
ResNet	0.637±0.000 (810s)	0.948±0.000 (1051s)	0.649±0.000 (185s)	0.983±0.000 (239s)	0.705±0.001 (226s)	0.989±0.000 (1967s)	0.871±0.001 (173s)	0.890±0.001 (315s)	0.719±0.001 (245s)
FTTransformerBucket	0.637±0.000 (8032s)	0.947±0.000 (6571s)	0.649±0.001 (714s)	0.986±0.000 (2138s)	0.651±0.060 (1473s)	0.832±0.153 (8248s)	0.866±0.001 (1531s)	0.877±0.000 (2960s)	0.688±0.001 (1983s)
ExcelFormer	OOM	0.948±0.000 (6278s)	0.653±0.000 (515s)	0.982±0.000 (2691s)	0.716±0.001 (1263s)	0.985±0.000 (34917s)	0.877±0.001 (3388s)	0.886±0.001 (3237s)	0.708±0.001 (2138s)
FTTransformer	0.632±0.001 (7669s)	0.946±0.001 (4613s)	0.652±0.000 (587s)	0.981±0.000 (3048s)	0.704±0.001 (980s)	0.984±0.001 (15615s)	0.871±0.002 (1424s)	0.878±0.002 (2933s)	0.713±0.001 (1656s)
TabNet	0.628±0.002 (282s)	0.945±0.001 (338s)	0.650±0.001 (91s)	0.977±0.000 (95s)	0.706±0.001 (62s)	0.993±0.000 (671s)	0.862±0.001 (80s)	0.889±0.000 (120s)	0.797±0.001 (93s)
TabTransformer	0.634±0.001 (10791s)	0.942±0.001 (10599s)	0.642±0.000 (128s)	0.980±0.000 (130s)	0.698±0.002 (742s)	0.968±0.002 (24409s)	0.867±0.000 (94s)	0.873±0.000 (1132s)	0.788±0.000 (147s)

scale: large

TODO: Add results.

task_type: regression

Metric: RMSE, lower the better.

scale: small

Experimental setting: 20 Optuna search trials. 50 epochs of training.

	dataset_0	dataset_1	dataset_2	dataset_3	dataset_4	dataset_5	dataset_6	dataset_7	dataset_8	dataset_9	dataset_10	dataset_11	dataset_12
XGBoost	0.247±0.000 (516s)	0.077±0.000 (14s)	0.167±0.000 (423s)	1.119±0.000 (1063s)	0.328±0.000 (2044s)	1.024±0.000 (47s)	0.292±0.000 (844s)	0.606±0.000 (1765s)	0.876±0.000 (2288s)	0.023±0.000 (1170s)	0.697±0.000 (248s)	0.865±0.000 (8s)	0.435±0.000 (22s)
CatBoost	0.265±0.000 (116s)	0.062±0.000 (129s)	0.128±0.000 (97s)	0.336±0.000 (103s)	0.346±0.000 (110s)	0.443±0.000 (97s)	0.375±0.000 (46s)	0.273±0.000 (693s)	0.881±0.000 (660s)	0.040±0.000 (80s)	0.756±0.000 (44s)	0.876±0.000 (110s)	0.439±0.000 (101s)
LightGBM	0.253±0.000 (38s)	0.054±0.000 (24s)	0.112±0.000 (10s)	0.302±0.000 (30s)	0.325±0.000 (30s)	0.384±0.000 (23s)	0.295±0.000 (15s)	0.272±0.000 (26s)	0.877±0.000 (16s)	0.011±0.000 (12s)	0.702±0.000 (13s)	0.863±0.000 (5s)	0.395±0.000 (40s)
Trompt	0.261±0.003 (8390s)	0.015±0.005 (3792s)	0.118±0.001 (3836s)	0.262±0.001 (10037s)	0.323±0.001 (9255s)	0.418±0.003 (9071s)	0.329±0.009 (2977s)	0.312±0.002 (21967s)	OOM	0.008±0.001 (1889s)	0.779±0.006 (775s)	0.874±0.004 (3723s)	0.424±0.005 (3185s)
ResNet	0.288±0.006 (220s)	0.018±0.003 (187s)	0.124±0.001 (135s)	0.268±0.001 (330s)	0.335±0.001 (471s)	0.434±0.004 (345s)	0.325±0.012 (178s)	0.324±0.004 (365s)	0.895±0.005 (142s)	0.036±0.002 (172s)	0.794±0.006 (120s)	0.875±0.004 (122s)	0.468±0.004 (303s)
FTTransformerBucket	0.325±0.008 (619s)	0.096±0.005 (290s)	0.360±0.354 (332s)	0.284±0.005 (768s)	0.342±0.004 (757s)	0.441±0.003 (835s)	0.345±0.007 (191s)	0.339±0.003 (3321s)	OOM	0.105±0.011 (199s)	0.807±0.010 (156s)	0.885±0.008 (820s)	0.468±0.006 (706s)
ExcelFormer	0.302±0.003 (703s)	0.099±0.003 (490s)	0.145±0.003 (587s)	0.382±0.011 (504s)	0.344±0.002 (1096s)	0.411±0.005 (469s)	0.359±0.016 (207s)	0.336±0.008 (5522s)	OOM	0.192±0.014 (317s)	0.794±0.005 (189s)	0.890±0.003 (1186s)	0.445±0.005 (550s)
FTTransformer	0.335±0.010 (338s)	0.161±0.022 (370s)	0.140±0.002 (244s)	0.277±0.004 (516s)	0.335±0.003 (973s)	0.445±0.003 (599s)	0.361±0.018 (286s)	0.345±0.005 (2443s)	OOM	0.106±0.012 (150s)	0.826±0.005 (121s)	0.896±0.007 (832s)	0.461±0.003 (647s)
TabNet	0.279±0.003 (68s)	0.224±0.016 (53s)	0.141±0.010 (34s)	0.275±0.002 (61s)	0.348±0.003 (110s)	0.451±0.007 (82s)	0.355±0.030 (49s)	0.332±0.004 (168s)	0.992±0.182 (53s)	0.015±0.002 (57s)	0.805±0.014 (27s)	0.885±0.013 (46s)	0.544±0.011 (112s)
TabTransformer	0.624±0.003 (1225s)	0.229±0.003 (1200s)	0.369±0.005 (52s)	0.340±0.004 (163s)	0.388±0.002 (1137s)	0.539±0.003 (100s)	0.619±0.005 (73s)	0.351±0.001 (125s)	0.893±0.005 (389s)	0.431±0.001 (489s)	0.819±0.002 (52s)	0.886±0.005 (46s)	0.545±0.004 (95s)

scale: medium

Experimental setting: 20 Optuna search trials for XGBoost and CatBoost. 5 Optuna search trials and 25 epochs training for deep learning models.

	0	1	2	3	4	5
XGBoost	1.100±0.000 (739s)	0.020±0.000 (8341s)	0.090±0.000 (3530s)	0.142±0.000 (3664s)	0.540±0.000 (21566s)	0.895±0.000 (948s)
CatBoost	0.669±0.000 (2037s)	0.018±0.000 (649s)	0.092±0.000 (391s)	0.145±0.000 (271s)	0.549±0.000 (1347s)	0.898±0.000 (122s)
LightGBM	0.660±0.000 (199s)	0.015±0.000 (86s)	0.085±0.000 (39s)	0.141±0.000 (35s)	0.524±0.000 (148s)	0.895±0.000 (7s)
Trompt	OOM	0.014±0.000 (19976s)	0.092±0.001 (4060s)	0.140±0.000 (3487s)	0.537±0.000 (26520s)	0.901±0.000 (2333s)
ResNet	0.676±0.000 (894s)	0.016±0.000 (548s)	0.101±0.001 (176s)	0.147±0.000 (503s)	0.555±0.003 (1121s)	0.903±0.000 (116s)
FTTransformerBucket	0.738±0.029 (17223s)	0.023±0.000 (2573s)	0.113±0.002 (645s)	0.147±0.000 (970s)	0.545±0.000 (3009s)	0.908±0.000 (360s)
ExcelFormer	0.667±0.000 (35946s)	0.064±0.019 (2355s)	0.119±0.003 (594s)	0.220±0.009 (1285s)	0.563±0.002 (2772s)	0.902±0.000 (288s)
FTTransformer	0.673±0.000 (18524s)	0.056±0.003 (3348s)	0.119±0.003 (396s)	0.141±0.000 (1049s)	0.561±0.001 (2403s)	0.907±0.002 (302s)
TabNet	0.683±0.001 (521s)	0.024±0.001 (437s)	0.115±0.003 (72s)	0.140±0.000 (319s)	0.549±0.001 (760s)	0.899±0.001 (37s)
TabTransformer	OOM	0.799±0.000 (2829s)	0.148±0.000 (720s)	0.708±0.000 (182s)	0.755±0.000 (4008s)	0.964±0.000 (599s)

scale: large

TODO: Add results.

task_type: multiclass_classification

Metric: Accuracy, the higher the better.

scale: medium

Experimental setting: 20 Optuna search trials for XGBoost and CatBoost. 5 Optuna search trials and 25 epochs training for deep learning models.

*Too slow: Taking more than a day for a single trial.

	0	1	2
XGBoost	Too slow*	Too slow*	Too slow*
CatBoost	Too slow*	Too slow*	Too slow*
LightGBM	Too slow*	Too slow*	Too slow*
Trompt	OOM	0.373±0.004 (9114s)	OOM
ResNet	0.951±0.000 (419s)	0.378±0.001 (171s)	0.723±0.001 (257s)
FTTransformerBucket	0.879±0.006 (9104s)	0.365±0.002 (1067s)	0.722±0.001 (2366s)
ExcelFormer	OOM	0.375±0.004 (2168s)	0.732±0.000 (4138s)
FTTransformer	0.923±0.003 (14517s)	0.357±0.001 (754s)	0.724±0.004 (2621s)
TabNet	0.934±0.005 (218s)	0.349±0.001 (64s)	0.716±0.001 (153s)
TabTransformer	0.950±0.000 (160s)	0.352±0.001 (98s)	0.705±0.003 (103s)

scale: large

TODO