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Datasets:
polinaeterna
/
tabular-benchmark

Tasks:
Tabular Classification
Tabular Regression
Modalities:
Tabular
Formats:
csv
Size:
1M - 10M
Libraries:
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polinaeterna HF staff merve HF staff commited on
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Duplicate from inria-soda/tabular-benchmark

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Co-authored-by: Merve Noyan <merve@users.noreply.huggingface.co>

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  1. .gitattributes +72 -0
  2. README.md +292 -0
  3. clf_cat/albert.csv +0 -0
  4. clf_cat/compas-two-years.csv +0 -0
  5. clf_cat/covertype.csv +3 -0
  6. clf_cat/default-of-credit-card-clients.csv +0 -0
  7. clf_cat/electricity.csv +0 -0
  8. clf_cat/eye_movements.csv +0 -0
  9. clf_cat/road-safety.csv +3 -0
  10. clf_num/Bioresponse.csv +0 -0
  11. clf_num/Diabetes130US.csv +0 -0
  12. clf_num/Higgs.csv +3 -0
  13. clf_num/MagicTelescope.csv +0 -0
  14. clf_num/MiniBooNE.csv +3 -0
  15. clf_num/bank-marketing.csv +0 -0
  16. clf_num/california.csv +0 -0
  17. clf_num/covertype.csv +3 -0
  18. clf_num/credit.csv +0 -0
  19. clf_num/default-of-credit-card-clients.csv +0 -0
  20. clf_num/electricity.csv +0 -0
  21. clf_num/eye_movements.csv +0 -0
  22. clf_num/heloc.csv +0 -0
  23. clf_num/house_16H.csv +0 -0
  24. clf_num/jannis.csv +3 -0
  25. clf_num/pol.csv +0 -0
  26. git +0 -0
  27. reg_cat/Airlines_DepDelay_1M.csv +3 -0
  28. reg_cat/Allstate_Claims_Severity.csv +3 -0
  29. reg_cat/Bike_Sharing_Demand.csv +0 -0
  30. reg_cat/Brazilian_houses.csv +0 -0
  31. reg_cat/Mercedes_Benz_Greener_Manufacturing.csv +0 -0
  32. reg_cat/SGEMM_GPU_kernel_performance.csv +3 -0
  33. reg_cat/abalone.csv +0 -0
  34. reg_cat/analcatdata_supreme.csv +4053 -0
  35. reg_cat/delays_zurich_transport.csv +3 -0
  36. reg_cat/diamonds.csv +0 -0
  37. reg_cat/house_sales.csv +0 -0
  38. reg_cat/medical_charges.csv +0 -0
  39. reg_cat/nyc-taxi-green-dec-2016.csv +3 -0
  40. reg_cat/particulate-matter-ukair-2017.csv +3 -0
  41. reg_cat/seattlecrime6.csv +0 -0
  42. reg_cat/topo_2_1.csv +3 -0
  43. reg_cat/visualizing_soil.csv +0 -0
  44. reg_num/Ailerons.csv +0 -0
  45. reg_num/Bike_Sharing_Demand.csv +0 -0
  46. reg_num/Brazilian_houses.csv +0 -0
  47. reg_num/MiamiHousing2016.csv +0 -0
  48. reg_num/abalone.csv +0 -0
  49. reg_num/cpu_act.csv +0 -0
  50. reg_num/delays_zurich_transport.csv +3 -0
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+ *.7z filter=lfs diff=lfs merge=lfs -text
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+ *.arrow filter=lfs diff=lfs merge=lfs -text
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+ *.bin filter=lfs diff=lfs merge=lfs -text
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+ *.bz2 filter=lfs diff=lfs merge=lfs -text
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+ *.ftz filter=lfs diff=lfs merge=lfs -text
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+ *.gz filter=lfs diff=lfs merge=lfs -text
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+ *.h5 filter=lfs diff=lfs merge=lfs -text
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+ *.joblib filter=lfs diff=lfs merge=lfs -text
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+ *.lfs.* filter=lfs diff=lfs merge=lfs -text
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+ *.lz4 filter=lfs diff=lfs merge=lfs -text
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+ *.mlmodel filter=lfs diff=lfs merge=lfs -text
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+ *.model filter=lfs diff=lfs merge=lfs -text
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+ *.msgpack filter=lfs diff=lfs merge=lfs -text
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+ *.npy filter=lfs diff=lfs merge=lfs -text
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+ *.onnx filter=lfs diff=lfs merge=lfs -text
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+ *.parquet filter=lfs diff=lfs merge=lfs -text
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+ *.pb filter=lfs diff=lfs merge=lfs -text
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+ *.pickle filter=lfs diff=lfs merge=lfs -text
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+ *.pkl filter=lfs diff=lfs merge=lfs -text
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+ *.pt filter=lfs diff=lfs merge=lfs -text
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+ *.pth filter=lfs diff=lfs merge=lfs -text
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+ *.rar filter=lfs diff=lfs merge=lfs -text
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+ *.safetensors filter=lfs diff=lfs merge=lfs -text
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+ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+ *.tar.* filter=lfs diff=lfs merge=lfs -text
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+ *.tflite filter=lfs diff=lfs merge=lfs -text
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+ *.tgz filter=lfs diff=lfs merge=lfs -text
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+ *.wasm filter=lfs diff=lfs merge=lfs -text
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+ *.xz filter=lfs diff=lfs merge=lfs -text
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+ *.zip filter=lfs diff=lfs merge=lfs -text
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+ *.zst filter=lfs diff=lfs merge=lfs -text
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+ *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - uncompressed
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+ *.pcm filter=lfs diff=lfs merge=lfs -text
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+ *.sam filter=lfs diff=lfs merge=lfs -text
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+ *.raw filter=lfs diff=lfs merge=lfs -text
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+ # Audio files - compressed
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+ *.aac filter=lfs diff=lfs merge=lfs -text
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+ *.flac filter=lfs diff=lfs merge=lfs -text
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+ *.mp3 filter=lfs diff=lfs merge=lfs -text
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+ *.ogg filter=lfs diff=lfs merge=lfs -text
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+ *.wav filter=lfs diff=lfs merge=lfs -text
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+ # Image files - uncompressed
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+ *.bmp filter=lfs diff=lfs merge=lfs -text
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+ *.gif filter=lfs diff=lfs merge=lfs -text
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+ *.png filter=lfs diff=lfs merge=lfs -text
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+ *.tiff filter=lfs diff=lfs merge=lfs -text
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+ # Image files - compressed
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+ *.jpg filter=lfs diff=lfs merge=lfs -text
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+ *.jpeg filter=lfs diff=lfs merge=lfs -text
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+ *.webp filter=lfs diff=lfs merge=lfs -text
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+ reg_num/year.csv filter=lfs diff=lfs merge=lfs -text
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+ reg_num/nyc-taxi-green-dec-2016.csv filter=lfs diff=lfs merge=lfs -text
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+ reg_num/superconduct.csv filter=lfs diff=lfs merge=lfs -text
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+ reg_num/isolet.csv filter=lfs diff=lfs merge=lfs -text
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+ clf_num/Higgs.csv filter=lfs diff=lfs merge=lfs -text
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+ clf_num/MiniBooNE.csv filter=lfs diff=lfs merge=lfs -text
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+ clf_num/jannis.csv filter=lfs diff=lfs merge=lfs -text
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+ clf_num/covertype.csv filter=lfs diff=lfs merge=lfs -text
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+ reg_cat/OnlineNewsPopularity.csv filter=lfs diff=lfs merge=lfs -text
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+ reg_cat/SGEMM_GPU_kernel_performance.csv filter=lfs diff=lfs merge=lfs -text
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+ reg_cat/nyc-taxi-green-dec-2016.csv filter=lfs diff=lfs merge=lfs -text
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+ reg_cat/particulate-matter-ukair-2017.csv filter=lfs diff=lfs merge=lfs -text
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+ clf_cat/road-safety.csv filter=lfs diff=lfs merge=lfs -text
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+ clf_cat/covertype.csv filter=lfs diff=lfs merge=lfs -text
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+ reg_num/delays_zurich_transport.csv filter=lfs diff=lfs merge=lfs -text
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+ reg_cat/Allstate_Claims_Severity.csv filter=lfs diff=lfs merge=lfs -text
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+ reg_cat/Airlines_DepDelay_1M.csv filter=lfs diff=lfs merge=lfs -text
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+ reg_cat/delays_zurich_transport.csv filter=lfs diff=lfs merge=lfs -text
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+ reg_cat/topo_2_1.csv filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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+
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+ ---
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+ annotations_creators: []
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+ license: []
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+ pretty_name: tabular_benchmark
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+ tags: []
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+ task_categories:
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+ - tabular-classification
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+ - tabular-regression
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+ configs:
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+ - config_name: clf_cat_albert
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+ data_files: clf_cat/albert.csv
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+ - config_name: clf_cat_compas-two-years
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+ data_files: clf_cat/compas-two-years.csv
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+ - config_name: clf_cat_covertype
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+ data_files: clf_cat/covertype.csv
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+ - config_name: clf_cat_default-of-credit-card-clients
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+ data_files: clf_cat/default-of-credit-card-clients.csv
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+ - config_name: clf_cat_electricity
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+ data_files: clf_cat/electricity.csv
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+ - config_name: clf_cat_eye_movements
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+ data_files: clf_cat/eye_movements.csv
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+ - config_name: clf_cat_road-safety
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+ data_files: clf_cat/road-safety.csv
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+ - config_name: clf_num_Bioresponse
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+ data_files: clf_num/Bioresponse.csv
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+ - config_name: clf_num_Diabetes130US
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+ data_files: clf_num/Diabetes130US.csv
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+ - config_name: clf_num_Higgs
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+ data_files: clf_num/Higgs.csv
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+ - config_name: clf_num_MagicTelescope
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+ data_files: clf_num/MagicTelescope.csv
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+ - config_name: clf_num_MiniBooNE
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+ data_files: clf_num/MiniBooNE.csv
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+ - config_name: clf_num_bank-marketing
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+ data_files: clf_num/bank-marketing.csv
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+ - config_name: clf_num_california
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+ data_files: clf_num/california.csv
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+ - config_name: clf_num_covertype
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+ data_files: clf_num/covertype.csv
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+ - config_name: clf_num_credit
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+ data_files: clf_num/credit.csv
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+ - config_name: clf_num_default-of-credit-card-clients
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+ data_files: clf_num/default-of-credit-card-clients.csv
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+ - config_name: clf_num_electricity
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+ data_files: clf_num/electricity.csv
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+ - config_name: clf_num_eye_movements
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+ data_files: clf_num/eye_movements.csv
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+ - config_name: clf_num_heloc
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+ data_files: clf_num/heloc.csv
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+ - config_name: clf_num_house_16H
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+ data_files: clf_num/house_16H.csv
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+ - config_name: clf_num_jannis
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+ data_files: clf_num/jannis.csv
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+ - config_name: clf_num_pol
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+ data_files: clf_num/pol.csv
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+ - config_name: reg_cat_Airlines_DepDelay_1M
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+ data_files: reg_cat/Airlines_DepDelay_1M.csv
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+ - config_name: reg_cat_Allstate_Claims_Severity
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+ data_files: reg_cat/Allstate_Claims_Severity.csv
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+ - config_name: reg_cat_Bike_Sharing_Demand
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+ data_files: reg_cat/Bike_Sharing_Demand.csv
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+ - config_name: reg_cat_Brazilian_houses
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+ data_files: reg_cat/Brazilian_houses.csv
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+ - config_name: reg_cat_Mercedes_Benz_Greener_Manufacturing
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+ data_files: reg_cat/Mercedes_Benz_Greener_Manufacturing.csv
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+ - config_name: reg_cat_SGEMM_GPU_kernel_performance
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+ data_files: reg_cat/SGEMM_GPU_kernel_performance.csv
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+ - config_name: reg_cat_abalone
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+ data_files: reg_cat/abalone.csv
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+ - config_name: reg_cat_analcatdata_supreme
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+ data_files: reg_cat/analcatdata_supreme.csv
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+ - config_name: reg_cat_delays_zurich_transport
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+ data_files: reg_cat/delays_zurich_transport.csv
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+ - config_name: reg_cat_diamonds
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+ data_files: reg_cat/diamonds.csv
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+ - config_name: reg_cat_house_sales
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+ data_files: reg_cat/house_sales.csv
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+ - config_name: reg_cat_medical_charges
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+ data_files: reg_cat/medical_charges.csv
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+ - config_name: reg_cat_nyc-taxi-green-dec-2016
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+ data_files: reg_cat/nyc-taxi-green-dec-2016.csv
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+ - config_name: reg_cat_particulate-matter-ukair-2017
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+ data_files: reg_cat/particulate-matter-ukair-2017.csv
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+ - config_name: reg_cat_seattlecrime6
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+ data_files: reg_cat/seattlecrime6.csv
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+ - config_name: reg_cat_topo_2_1
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+ data_files: reg_cat/topo_2_1.csv
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+ - config_name: reg_cat_visualizing_soil
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+ data_files: reg_cat/visualizing_soil.csv
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+ - config_name: reg_num_Ailerons
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+ data_files: reg_num/Ailerons.csv
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+ - config_name: reg_num_Bike_Sharing_Demand
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+ data_files: reg_num/Bike_Sharing_Demand.csv
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+ - config_name: reg_num_Brazilian_houses
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+ data_files: reg_num/Brazilian_houses.csv
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+ - config_name: reg_num_MiamiHousing2016
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+ data_files: reg_num/MiamiHousing2016.csv
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+ - config_name: reg_num_abalone
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+ data_files: reg_num/abalone.csv
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+ - config_name: reg_num_cpu_act
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+ data_files: reg_num/cpu_act.csv
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+ - config_name: reg_num_delays_zurich_transport
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+ data_files: reg_num/delays_zurich_transport.csv
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+ - config_name: reg_num_diamonds
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+ data_files: reg_num/diamonds.csv
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+ - config_name: reg_num_elevators
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+ data_files: reg_num/elevators.csv
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+ - config_name: reg_num_house_16H
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+ data_files: reg_num/house_16H.csv
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+ - config_name: reg_num_house_sales
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+ data_files: reg_num/house_sales.csv
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+ - config_name: reg_num_houses
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+ data_files: reg_num/houses.csv
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+ - config_name: reg_num_medical_charges
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+ data_files: reg_num/medical_charges.csv
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+ - config_name: reg_num_nyc-taxi-green-dec-2016
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+ data_files: reg_num/nyc-taxi-green-dec-2016.csv
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+ - config_name: reg_num_pol
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+ data_files: reg_num/pol.csv
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+ - config_name: reg_num_sulfur
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+ data_files: reg_num/sulfur.csv
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+ - config_name: reg_num_superconduct
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+ data_files: reg_num/superconduct.csv
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+ - config_name: reg_num_wine_quality
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+ data_files: reg_num/wine_quality.csv
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+ - config_name: reg_num_yprop_4_1
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+ data_files: reg_num/yprop_4_1.csv
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+ ---
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+
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+ # Tabular Benchmark
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+
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+ ## Dataset Description
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+
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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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+
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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 Summary
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+
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+ Benchmark made of curation of various tabular data learning tasks, including:
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+ - Regression from Numerical and Categorical Features
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+ - Regression from Numerical Features
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+ - Classification from Numerical and Categorical Features
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+ - Classification from Numerical Features
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+
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+ ### Supported Tasks and Leaderboards
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+
150
+ - `tabular-regression`
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+ - `tabular-classification`
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+
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+ ## Dataset Structure
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+
155
+ ### Data Splits
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+
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+ This dataset consists of four splits (folders) based on tasks and datasets included in tasks.
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+
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+ - reg_num: Task identifier for regression on numerical features.
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+ - reg_cat: Task identifier for regression on numerical and categorical features.
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+ - clf_num: Task identifier for classification on numerical features.
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+ - clf_cat: Task identifier for classification on categorical features.
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+
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+ Depending on the dataset you want to load, you can load the dataset by passing `task_name/dataset_name` to `data_files` argument of `load_dataset` like below:
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+
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+ ```python
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+ from datasets import load_dataset
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+ dataset = load_dataset("inria-soda/tabular-benchmark", data_files="reg_cat/house_sales.csv")
169
+ ```
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+
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+
172
+ ## Dataset Creation
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+
174
+ ### Curation Rationale
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+
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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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+
178
+ - **Heterogeneous columns**. Columns should correspond to features of different nature. This excludes
179
+ images or signal datasets where each column corresponds to the same signal on different sensors.
180
+ - **Not high dimensional**. We only keep datasets with a d/n ratio below 1/10.
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+ - **Undocumented datasets** We remove datasets where too little information is available. We did keep
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+ datasets with hidden column names if it was clear that the features were heterogeneous.
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+ - **I.I.D. data**. We remove stream-like datasets or time series.
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+ - **Real-world data**. We remove artificial datasets but keep some simulated datasets. The difference is
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+ subtle, but we try to keep simulated datasets if learning these datasets are of practical importance
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+ (like the Higgs dataset), and not just a toy example to test specific model capabilities.
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+ - **Not too small**. We remove datasets with too few features (< 4) and too few samples (< 3 000). For
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+ benchmarks on numerical features only, we remove categorical features before checking if enough
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+ features and samples are remaining.
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+ - **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)
191
+ 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)
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+ 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],
193
+ but this ignores varying Bayes rate across datasets. As tree ensembles are superior to simple trees and logistic regresison [Fernández-Delgado et al., 2014],
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+ a close score for the simple and powerful models suggests that we are already close to the best achievable score.
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+ - **Not deterministic**. We remove datasets where the target is a deterministic function of the data. This
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+ mostly means removing datasets on games like poker and chess. Indeed, we believe that these
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+ datasets are very different from most real-world tabular datasets, and should be studied separately
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+
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+ ### Source Data
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+
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+
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+ **Numerical Classification**
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+ |dataset_name|n_samples|n_features|original_link|new_link|
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+ |---|---|---|---|---|
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+ |electricity|38474.0|7.0|https://www.openml.org/d/151|https://www.openml.org/d/44120|
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+ |covertype|566602.0|10.0|https://www.openml.org/d/293|https://www.openml.org/d/44121|
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+ |pol|10082.0|26.0|https://www.openml.org/d/722|https://www.openml.org/d/44122|
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+ |house_16H|13488.0|16.0|https://www.openml.org/d/821|https://www.openml.org/d/44123|
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+ |MagicTelescope|13376.0|10.0|https://www.openml.org/d/1120|https://www.openml.org/d/44125|
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+ |bank-marketing|10578.0|7.0|https://www.openml.org/d/1461|https://www.openml.org/d/44126|
211
+ |Bioresponse|3434.0|419.0|https://www.openml.org/d/4134|https://www.openml.org/d/45019|
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+ |MiniBooNE|72998.0|50.0|https://www.openml.org/d/41150|https://www.openml.org/d/44128|
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+ |default-of-credit-card-clients|13272.0|20.0|https://www.openml.org/d/42477|https://www.openml.org/d/45020|
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+ |Higgs|940160.0|24.0|https://www.openml.org/d/42769|https://www.openml.org/d/44129|
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+ |eye_movements|7608.0|20.0|https://www.openml.org/d/1044|https://www.openml.org/d/44130|
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+ |Diabetes130US|71090.0|7.0|https://www.openml.org/d/4541|https://www.openml.org/d/45022|
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+ |jannis|57580.0|54.0|https://www.openml.org/d/41168|https://www.openml.org/d/45021|
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+ |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|
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+ |credit|16714.0|10.0|"https://www.kaggle.com/c/GiveMeSomeCredit/data?select=cs-training.csv"|https://www.openml.org/d/44089|
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+ |california|20634.0|8.0|"https://www.dcc.fc.up.pt/ltorgo/Regression/cal_housing.html"|https://www.openml.org/d/45028|
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+
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+
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+ **Categorical Classification**
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+ |dataset_name|n_samples|n_features|original_link|new_link|
225
+ |---|---|---|---|---|
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+ |electricity|38474.0|8.0|https://www.openml.org/d/151|https://www.openml.org/d/44156|
227
+ |eye_movements|7608.0|23.0|https://www.openml.org/d/1044|https://www.openml.org/d/44157|
228
+ |covertype|423680.0|54.0|https://www.openml.org/d/1596|https://www.openml.org/d/44159|
229
+ |albert|58252.0|31.0|https://www.openml.org/d/41147|https://www.openml.org/d/45035|
230
+ |compas-two-years|4966.0|11.0|https://www.openml.org/d/42192|https://www.openml.org/d/45039|
231
+ |default-of-credit-card-clients|13272.0|21.0|https://www.openml.org/d/42477|https://www.openml.org/d/45036|
232
+ |road-safety|111762.0|32.0|https://www.openml.org/d/42803|https://www.openml.org/d/45038|
233
+
234
+
235
+ **Numerical Regression**
236
+ |dataset_name|n_samples|n_features|original_link|new_link|
237
+ |---|---|---|---|---|
238
+ |cpu_act|8192.0|21.0|https://www.openml.org/d/197|https://www.openml.org/d/44132|
239
+ |pol|15000.0|26.0|https://www.openml.org/d/201|https://www.openml.org/d/44133|
240
+ |elevators|16599.0|16.0|https://www.openml.org/d/216|https://www.openml.org/d/44134|
241
+ |wine_quality|6497.0|11.0|https://www.openml.org/d/287|https://www.openml.org/d/44136|
242
+ |Ailerons|13750.0|33.0|https://www.openml.org/d/296|https://www.openml.org/d/44137|
243
+ |yprop_4_1|8885.0|42.0|https://www.openml.org/d/416|https://www.openml.org/d/45032|
244
+ |houses|20640.0|8.0|https://www.openml.org/d/537|https://www.openml.org/d/44138|
245
+ |house_16H|22784.0|16.0|https://www.openml.org/d/574|https://www.openml.org/d/44139|
246
+ |delays_zurich_transport|5465575.0|9.0|https://www.openml.org/d/40753|https://www.openml.org/d/45034|
247
+ |diamonds|53940.0|6.0|https://www.openml.org/d/42225|https://www.openml.org/d/44140|
248
+ |Brazilian_houses|10692.0|8.0|https://www.openml.org/d/42688|https://www.openml.org/d/44141|
249
+ |Bike_Sharing_Demand|17379.0|6.0|https://www.openml.org/d/42712|https://www.openml.org/d/44142|
250
+ |nyc-taxi-green-dec-2016|581835.0|9.0|https://www.openml.org/d/42729|https://www.openml.org/d/44143|
251
+ |house_sales|21613.0|15.0|https://www.openml.org/d/42731|https://www.openml.org/d/44144|
252
+ |sulfur|10081.0|6.0|https://www.openml.org/d/23515|https://www.openml.org/d/44145|
253
+ |medical_charges|163065.0|5.0|https://www.openml.org/d/42720|https://www.openml.org/d/44146|
254
+ |MiamiHousing2016|13932.0|14.0|https://www.openml.org/d/43093|https://www.openml.org/d/44147|
255
+ |superconduct|21263.0|79.0|https://www.openml.org/d/43174|https://www.openml.org/d/44148|
256
+
257
+
258
+ **Categorical Regression**
259
+ |dataset_name|n_samples|n_features|original_link|new_link|
260
+ |---|---|---|---|---|
261
+ |topo_2_1|8885.0|255.0|https://www.openml.org/d/422|https://www.openml.org/d/45041|
262
+ |analcatdata_supreme|4052.0|7.0|https://www.openml.org/d/504|https://www.openml.org/d/44055|
263
+ |visualizing_soil|8641.0|4.0|https://www.openml.org/d/688|https://www.openml.org/d/44056|
264
+ |delays_zurich_transport|5465575.0|12.0|https://www.openml.org/d/40753|https://www.openml.org/d/45045|
265
+ |diamonds|53940.0|9.0|https://www.openml.org/d/42225|https://www.openml.org/d/44059|
266
+ |Allstate_Claims_Severity|188318.0|124.0|https://www.openml.org/d/42571|https://www.openml.org/d/45046|
267
+ |Mercedes_Benz_Greener_Manufacturing|4209.0|359.0|https://www.openml.org/d/42570|https://www.openml.org/d/44061|
268
+ |Brazilian_houses|10692.0|11.0|https://www.openml.org/d/42688|https://www.openml.org/d/44062|
269
+ |Bike_Sharing_Demand|17379.0|11.0|https://www.openml.org/d/42712|https://www.openml.org/d/44063|
270
+ |Airlines_DepDelay_1M|1000000.0|5.0|https://www.openml.org/d/42721|https://www.openml.org/d/45047|
271
+ |nyc-taxi-green-dec-2016|581835.0|16.0|https://www.openml.org/d/42729|https://www.openml.org/d/44065|
272
+ |abalone|4177.0|8.0|https://www.openml.org/d/42726|https://www.openml.org/d/45042|
273
+ |house_sales|21613.0|17.0|https://www.openml.org/d/42731|https://www.openml.org/d/44066|
274
+ |seattlecrime6|52031.0|4.0|https://www.openml.org/d/42496|https://www.openml.org/d/45043|
275
+ |medical_charges|163065.0|5.0|https://www.openml.org/d/42720|https://www.openml.org/d/45048|
276
+ |particulate-matter-ukair-2017|394299.0|6.0|https://www.openml.org/d/42207|https://www.openml.org/d/44068|
277
+ |SGEMM_GPU_kernel_performance|241600.0|9.0|https://www.openml.org/d/43144|https://www.openml.org/d/44069|
278
+
279
+
280
+ ### Dataset Curators
281
+
282
+ Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux.
283
+
284
+ ### Licensing Information
285
+
286
+ [More Information Needed]
287
+
288
+ ### Citation Information
289
+
290
+ Léo Grinsztajn, Edouard Oyallon, Gaël Varoquaux. Why do tree-based models still outperform deep
291
+ learning on typical tabular data?. NeurIPS 2022 Datasets and Benchmarks Track, Nov 2022, New
292
+ Orleans, United States. ffhal-03723551v2f
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1759
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1760
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1761
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1762
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1763
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1764
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1765
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1766
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1767
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1768
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1769
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1770
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1771
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1772
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1773
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1774
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1775
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1776
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1777
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1778
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1779
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1780
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1781
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1782
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1783
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1784
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1785
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1786
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1787
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1788
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1789
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1790
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1791
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1792
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1793
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1794
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1795
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1796
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1797
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1798
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1799
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1800
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1801
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1802
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1803
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1804
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1805
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1806
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1807
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1808
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1809
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1810
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1811
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1812
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1813
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1814
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1815
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1816
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1817
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1818
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1819
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1820
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1821
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1822
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1823
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1824
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1825
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1826
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1827
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1828
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1829
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1830
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1831
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1832
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1833
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1834
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1835
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1836
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1837
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1838
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1839
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1840
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1841
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1842
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1843
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1844
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1845
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1846
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1847
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1848
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1849
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1850
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1851
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1852
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1853
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1854
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1855
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1856
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1857
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1858
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1859
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1860
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1861
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1862
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1863
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1864
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1865
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1866
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1867
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1868
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1869
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1870
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1871
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1872
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1873
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1874
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1875
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1876
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1877
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1878
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1879
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1880
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1881
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1882
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1883
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1884
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1885
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1886
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1887
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1888
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1889
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1890
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1891
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1892
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1893
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1894
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1895
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1896
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1897
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1898
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1899
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1900
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1901
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1902
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1903
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1904
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1905
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1906
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1907
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1908
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1909
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1910
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1911
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1912
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1913
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1914
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1915
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1916
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1917
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1918
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1919
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1920
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1921
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1922
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1923
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1924
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1925
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1926
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1927
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1928
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1929
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1930
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1931
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1932
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1933
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1934
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1935
+ 0,0,0,0,0,1972.0,0,2.3
1936
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1937
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1938
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1939
+ 0,1,0,0,1,1972.0,0,2.3
1940
+ 0,0,0,1,0,1972.0,0,2.3
1941
+ 0,1,0,0,1,1972.0,0,2.3
1942
+ 0,1,0,0,1,1972.0,0,2.3
1943
+ 0,0,0,0,0,1972.0,0,2.3
1944
+ 0,0,0,0,0,1972.0,1,2.3
1945
+ 0,1,1,0,0,1972.0,0,2.3
1946
+ 0,0,0,0,0,1972.0,0,2.3
1947
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1948
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1949
+ 0,0,0,0,0,1972.0,0,2.3
1950
+ 0,0,0,0,1,1972.0,1,2.3
1951
+ 0,0,0,0,0,1972.0,1,2.3
1952
+ 0,0,0,0,0,1972.0,0,2.3
1953
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1954
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1955
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1956
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1957
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1958
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1959
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1960
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1961
+ 0,1,0,0,0,1973.0,0,2.3
1962
+ 0,0,0,0,0,1973.0,0,2.3
1963
+ 0,0,0,0,0,1973.0,0,2.3
1964
+ 0,0,0,0,0,1973.0,1,2.3
1965
+ 0,0,0,0,0,1973.0,1,2.3
1966
+ 0,0,0,0,0,1973.0,1,2.3
1967
+ 0,0,0,0,0,1973.0,1,2.3
1968
+ 0,0,0,0,0,1973.0,0,2.3
1969
+ 0,0,0,0,0,1973.0,1,2.3
1970
+ 0,1,1,0,0,1973.0,0,2.3
1971
+ 0,1,1,0,0,1973.0,1,2.3
1972
+ 0,1,0,0,1,1973.0,0,2.3
1973
+ 0,1,1,0,0,1973.0,1,2.3
1974
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1975
+ 0,1,0,0,1,1973.0,0,2.3
1976
+ 0,0,0,0,0,1973.0,0,2.3
1977
+ 0,0,0,0,0,1973.0,1,2.3
1978
+ 0,1,0,0,1,1973.0,0,2.3
1979
+ 0,0,0,0,0,1973.0,0,2.3
1980
+ 0,1,1,0,0,1973.0,0,2.3
1981
+ 0,0,0,0,0,1973.0,0,2.3
1982
+ 5,1,0,0,0,1973.0,0,2.3
1983
+ 0,0,0,0,0,1973.0,0,2.3
1984
+ 0,0,0,0,0,1973.0,1,2.3
1985
+ 0,0,0,0,0,1973.0,0,2.3
1986
+ 0,1,1,0,1,1973.0,0,2.3
1987
+ 0,0,0,0,0,1973.0,0,2.3
1988
+ 1,0,0,0,0,1973.0,1,2.3
1989
+ 0,1,0,0,0,1973.0,1,2.3
1990
+ 0,1,0,0,1,1973.0,0,2.3
1991
+ 0,1,0,0,0,1973.0,0,2.3
1992
+ 0,0,0,0,0,1973.0,1,2.3
1993
+ 0,0,0,0,0,1973.0,0,2.3
1994
+ 0,1,0,0,0,1973.0,0,2.3
1995
+ 0,1,1,0,0,1973.0,1,2.3
1996
+ 0,0,0,0,0,1973.0,0,2.3
1997
+ 0,1,0,0,0,1973.0,0,2.3
1998
+ 0,0,0,0,0,1973.0,0,2.3
1999
+ 0,0,0,0,1,1973.0,1,2.3
2000
+ 0,1,1,0,0,1973.0,1,2.3
2001
+ 0,0,0,0,0,1973.0,0,2.3
2002
+ 0,1,0,0,1,1973.0,1,2.3
2003
+ 0,0,0,0,0,1973.0,0,2.3
2004
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2005
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2006
+ 0,0,0,0,0,1973.0,0,2.3
2007
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2008
+ 0,0,0,0,0,1973.0,0,2.3
2009
+ 0,0,0,0,0,1973.0,0,2.3
2010
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2011
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2012
+ 0,0,0,0,1,1973.0,1,2.3
2013
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2014
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2015
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2016
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2017
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2018
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2019
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2020
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2021
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2022
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2023
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2024
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2025
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2026
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2027
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2028
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2029
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2030
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2031
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2032
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2033
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2034
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2035
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2036
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2037
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2038
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2039
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2040
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2041
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2042
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2043
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2044
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2045
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2046
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2047
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2048
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2049
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2050
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2051
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2052
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2053
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2054
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2055
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2056
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2057
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2058
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2059
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2060
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2061
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2062
+ 0,1,0,0,0,1973.0,0,2.3
2063
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2064
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2065
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2066
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2067
+ 0,0,0,0,0,1973.0,0,2.3
2068
+ 0,0,0,0,0,1973.0,0,2.3
2069
+ 0,1,0,0,0,1973.0,0,2.3
2070
+ 0,1,0,0,0,1973.0,1,2.3
2071
+ 0,1,0,0,0,1973.0,0,2.3
2072
+ 0,1,1,0,1,1973.0,0,2.3
2073
+ 0,1,0,0,0,1973.0,0,2.3
2074
+ 0,0,0,0,0,1973.0,0,2.3
2075
+ 0,0,0,0,0,1973.0,0,2.3
2076
+ 0,0,0,0,0,1973.0,1,2.3
2077
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2078
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2645
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2694
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2695
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2696
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2697
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2698
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2699
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2700
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2701
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2702
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2703
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2704
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2705
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2706
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2707
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2708
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2709
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2710
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2711
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2712
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2713
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2714
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2715
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2716
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2717
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2718
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2719
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2720
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2721
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2722
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2723
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2724
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2725
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2726
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2727
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2728
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2729
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2730
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2731
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2732
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2733
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2734
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2735
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2736
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2737
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2738
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2739
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2740
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2741
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2742
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2743
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2744
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2745
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2746
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2747
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2748
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2749
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2750
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2751
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2752
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2753
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2754
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2755
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2756
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2757
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2758
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2759
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2760
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2761
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2762
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2763
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2764
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2765
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2766
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2767
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2768
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2769
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2770
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2771
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2772
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2773
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2775
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2776
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2779
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2780
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2781
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2783
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2795
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2798
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2799
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2800
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2801
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2802
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2803
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2804
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2805
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2806
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2807
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2808
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2809
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2810
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2811
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2812
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2813
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2814
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2816
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2817
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2818
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2819
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2820
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2821
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2822
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2823
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2824
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2825
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2826
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2827
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2828
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2829
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2830
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2831
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2832
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2833
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2834
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2835
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2836
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2837
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2838
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2839
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2840
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2841
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2842
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2843
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2844
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2845
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2846
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2847
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2848
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2849
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2850
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2851
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2852
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2853
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2854
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2855
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2856
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2857
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2858
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2859
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2860
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2861
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2862
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2863
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2864
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2865
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2866
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2867
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2868
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2869
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2870
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2871
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2872
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2873
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2874
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2875
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2876
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2877
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2878
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2879
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2880
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2881
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2882
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2883
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2884
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2885
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2886
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2887
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2888
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2889
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2890
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2891
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2892
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2893
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2894
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2895
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2896
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2897
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2898
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2899
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2900
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2901
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2902
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2903
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2904
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2905
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2906
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2907
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2908
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2909
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2910
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2911
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2912
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2913
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2914
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2915
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2916
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2917
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2918
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2919
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2920
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2921
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2922
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2923
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2924
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2925
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2926
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2927
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2928
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2929
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2930
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2931
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2932
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2933
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2934
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2935
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2936
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2937
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2938
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2939
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2940
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2941
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2942
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2943
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2944
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2945
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2946
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2947
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2948
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2949
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2950
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2951
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2952
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2953
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2954
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2955
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2956
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2957
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2958
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2959
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2960
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2961
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2962
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2963
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2964
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2965
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2966
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2967
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2968
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2969
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2970
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2971
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2972
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2973
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2974
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2975
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2976
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2977
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2978
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2979
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2980
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2981
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2982
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2983
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2984
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2985
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2986
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2987
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2988
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2989
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2990
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2991
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2992
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2993
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2994
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2995
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2996
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2997
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2998
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2999
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3000
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3001
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3002
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3003
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3004
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3005
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3006
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3007
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3008
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3009
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3010
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3011
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3012
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3013
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3014
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3015
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3016
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3017
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3018
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3019
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