Scikit-learn
English
benchmark
sports-analytics
speedcubing
wca
tabular
time-series
causal-inference
machine-learning
Instructions to use Maicarons/WCA-Bench-baselines with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use Maicarons/WCA-Bench-baselines with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Maicarons/WCA-Bench-baselines", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
Download leaderboard.csv from Maicarons/WCA-Bench-baselines: direct link, hf CLI and curl.
- Browser
- Download file 1.62 kB
-
https://huggingface.co/Maicarons/WCA-Bench-baselines/resolve/main/leaderboard.csv
- Command line
-
hf download hf://Maicarons/WCA-Bench-baselines/leaderboard.csv
-
curl -L -o leaderboard.csv https://huggingface.co/Maicarons/WCA-Bench-baselines/resolve/main/leaderboard.csv
1.62 kB
| task,model,primary_metric,primary_value,direction,n,failed,error,_rank | |
| dnf,xgboost_dnf,auc_pr,0.37305420320583965,higher,2260.0,False,,1.0 | |
| dnf,beta_binomial,auc_pr,0.3724876339218829,higher,2260.0,False,,2.0 | |
| dnf,historical_dnf_rate,auc_pr,0.3590637561741547,higher,2260.0,False,,3.0 | |
| dnf,logistic,auc_pr,0.22895999087860608,higher,2260.0,False,,4.0 | |
| limit,gp_evt,mean_loo_std,0.9836075126847903,lower,,False,,1.0 | |
| limit,exponential_decay,mean_loo_std,6485301.013726644,lower,,False,,2.0 | |
| limit,hierarchical_shrinkage,mean_loo_std,9260100.37457967,lower,,False,,3.0 | |
| limit,changepoint,mean_loo_std,,lower,,False,, | |
| placement,kde_simulation,kendall_tau,0.7673476295148504,higher,2260.0,False,,2.0 | |
| placement,plackett_luce,kendall_tau,0.7673476295148504,higher,2260.0,False,,2.0 | |
| placement,psych_sheet,kendall_tau,0.7673476295148504,higher,2260.0,False,,2.0 | |
| placement,gnn,kendall_tau,0.5670051979752496,higher,2260.0,False,,4.0 | |
| result_prediction,xgboost_log,mae_log,0.16385697572203922,lower,2196.0,False,,1.0 | |
| result_prediction,history_mean,mae_log,0.6657161932188251,lower,2196.0,False,,2.5 | |
| result_prediction,kde,mae_log,0.6657161932188251,lower,2196.0,False,,2.5 | |
| result_prediction,ridge_log,mae_log,1.006803993797732,lower,2196.0,False,,4.0 | |
| result_prediction,lstm,mae_log,1.044039947679419,lower,2196.0,False,,5.0 | |
| transfer,spearman_correlation,n_identified_pairs,413.0,higher,80000.0,False,,1.0 | |
| transfer,causal_forest,n_identified_pairs,312.0,higher,80000.0,False,,2.0 | |
| transfer,iv_2sls,n_identified_pairs,270.0,higher,80000.0,False,,3.0 | |
| transfer,did_proxy,n_identified_pairs,6.0,higher,80000.0,False,,4.0 | |