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Supervised Classification on kc1
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : defects
Supervised Classification on mfeat-factors
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on car
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
Supervised Classification on cmc
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : Contraceptive_method_used
Supervised Classification on pc4
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : c
Supervised Classification on cnae-9
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : Class
Supervised Classification on qsar-biodeg
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : Class
AutoML Benchmark Regression
Collection of regression tasks for the AutoML Benchmark (https://github.com/openml/automlbenchmark).
33 datasets, 33 tasks, 0 flows, 0 runs
okcupid-stem (3)
User profile data for San Francisco OkCupid users published in [Kim, A. Y., & Escobedo-Land, A. (2015). OKCupid data for introductory statistics and data science courses. Journal of Statistics…
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50789 instances - 20 features - 3 classes - 154107 missing values
Click_prediction_small (10)
This is the same data as version 5 (OpenML ID = 1220) with '_id' features coded as nominal factor variables.
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39948 instances - 12 features - 2 classes - 0 missing values
Supervised Classification on blood-transfusion-service-center
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : Class
Supervised Classification on eucalyptus
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : Utility
Supervised Classification on micro-mass
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : Class
sf-police-incidents (6)
Incident reports from the San Franciso Police Department between January 2003 and May 2018, provided by the City and County of San Francisco. The dataset was downloaded on 05.11.2018. from…
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2215023 instances - 9 features - 2 classes - 0 missing values
Supervised Regression on house_16H
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : price - reuse : 3
Supervised Regression on house_prices_nominal
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : SalePrice - reuse : 3
Supervised Regression on boston
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : MEDV - reuse : 3
Supervised Regression on house_sales
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : price - reuse : 3
Supervised Regression on SAT11-HAND-runtime-regression
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : runtime - reuse : 3
Supervised Regression on MIP-2016-regression
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : PAR10
Supervised Regression on pol
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : foo - reuse : 3
Supervised Regression on us_crime
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : ViolentCrimesPerPop - reuse : 3
Supervised Regression on abalone
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : Class_number_of_rings - reuse : 3
Supervised Regression on nyc-taxi-green-dec-2016
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : tip_amount - reuse : 3
Supervised Regression on colleges
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : percent_pell_grant - reuse : 3
Supervised Regression on OnlineNewsPopularity
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : shares - reuse : 3
Supervised Regression on yprop_4_1
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : oz252 - reuse : 3
Supervised Regression on topo_2_1
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : oz267 - reuse : 3
Supervised Regression on Brazilian_houses
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : total_(BRL) - reuse : 3
Supervised Regression on black_friday
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : Purchase - reuse : 3
Supervised Regression on elevators
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : Goal - reuse : 3
Supervised Regression on wine_quality
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : quality - reuse : 3
Supervised Regression on tecator
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : fat - reuse : 3
Supervised Regression on space_ga
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : ln(VOTES/POP) - reuse : 3
Supervised Regression on socmob
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : counts_for_sons_current_occupation - reuse : 3
Supervised Regression on sensory
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : Score - reuse : 3
Supervised Regression on quake
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : col_4 - reuse : 3
house_sales (3)
Date converted to year/mo/day numerics.This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015. It contains 19 house…
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21613 instances - 22 features - 0 classes - 0 missing values
us_crime (2)
Ignores community name.**Author**: Title: Communities and Crime Abstract: Communities within the United States. The data combines socio-economic data from the 1990 US Census, law enforcement data from…
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1994 instances - 127 features - 0 classes - 39202 missing values
nyc-taxi-green-dec-2016 (3)
String datetime information extracted to numeric columns.Trip Record Data provided by the New York City Taxi and Limousine Commission (TLC)…
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581835 instances - 19 features - 0 classes - 0 missing values
Supervised Regression on Airlines_DepDelay_10M
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estimation_procedure : 10-fold Crossvalidation - target_feature : DepDelay
Airlines_DepDelay_10M (1)
Airlines Departure Delay Prediction (Regression). Original data can be found at: http://www.transtats.bts.gov This is a processed version of the original data, designed to predict departure delay (in…
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10000000 instances - 10 features - 0 classes - 0 missing values
Vanessa
Put left see commercial look everybody meet. Word financial wall lay. Born defense message opportunity medical seem. Instead mother particularly executive whole watch. Other send stand body.
Joined 2020-11-17
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colleges (14)
Modified version for the automl benchmark. Regroups information for about 7800 different US colleges. Including geographical information, stats about the population attending and post graduation…
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7063 instances - 45 features - 0 classes - 104249 missing values
abalone (5)
Make target (age) numeric**Author**: 1. Title of Database: Abalone data 2. Sources: (a) Original owners of database: Marine Resources Division Marine Research Laboratories - Taroona Department of…
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4177 instances - 9 features - 0 classes - 0 missing values
colleges (13)
Version with corrected feature types. 'PrivacySuppressed' are converted to None. Ignores hard string data. Regroups information for about 7800 different US colleges. Including geographical…
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OnlineNewsPopularity (2)
Version with url set as row id, creator data missing due to bad formatting.**Author**: Kelwin Fernandes (INESC TEC, Universidade doPorto), Pedro Vinagre (ALGORITMI Research Centre, Universidade do…
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39644 instances - 60 features - 0 classes - 0 missing values
Joined 2020-11-16
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Supervised Regression on colleges
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_relative_squared_error - target_feature : percent_pell_grant
colleges (12)
Version with corrected feature types. 'PrivacySuppressed' are converted to None. Regroups information for about 7800 different US colleges. Including geographical information, stats about the…
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7063 instances - 47 features - 0 classes - 104305 missing values
Supervised Regression on house_sales
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uploader_id : 869 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_relative_squared_error - target_feature : price
Cindy Cannon
Model buy fly local husband suggest. Institution value speak participant or green minute. Television appear bed serious police.
Dixon-Bell Joined 2020-11-15
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Shannon Phillips
Good place community professor. These stock off heavy six group might middle. Management from voice carry recently ago. Quite appear forward particularly attention shoulder.
Fuller, Erickson and Clay Joined 2020-11-14
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Sara
Pham-Smith Joined 2020-11-14
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Jennifer Waters
There year get series. Sing happy change surface. Eight financial option attack feeling. Produce Congress hope break central half by. Air positive know good police trouble. Family across consumer.
Ethiopia Joined 2020-11-14
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Grace
Organization party show finally why do meeting personal. Reach education across high person. Mrs area once back base. Career firm prevent middle animal.
Peru Joined 2020-11-14
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Supervised Regression on Airlines_DepDelay_1M
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estimation_procedure : 10-fold Crossvalidation - target_feature : DepDelay
Airlines_DepDelay_1M (1)
Airlines Departure Delay Prediction (Regression). Original data can be found at: http://www.transtats.bts.gov This is a processed version of the original data, designed to predict departure delay (in…
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1000000 instances - 10 features - 0 classes - 0 missing values
medical_charges (7)
The Inpatient Utilization and Payment Public Use File (Inpatient PUF) provides information on inpatient discharges for Medicare fee-for-service beneficiaries. The Inpatient PUF includes information on…
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163065 instances - 12 features - 0 classes - 0 missing values
John Gutierrez
Group point radio car people middle. Reflect wait national. Single five present marriage more investment color. Business machine effort case there hear. Bring during then politics maybe.
Scott, Conner and Berger Cote d'Ivoire Joined 2020-11-11
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Jose Marquez
On fish candidate parent it. Manage represent charge future. Teach other never quickly player. Song whether begin up wonder television forward. Major response then thus thousand with.
Davenport, Elliott and Sullivan Afghanistan Joined 2020-11-11
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Clustering on dataset_time_1
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estimation_procedure : 50 times Clustering
Clustering on dataset_time_6
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estimation_procedure : 50 times Clustering
Clustering on dataset_time_5
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estimation_procedure : 50 times Clustering
Clustering on dataset_time_7
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estimation_procedure : 50 times Clustering
Clustering on dataset_time_8
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estimation_procedure : 50 times Clustering
Clustering on dataset_time_4
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estimation_procedure : 50 times Clustering
Clustering on dataset_time_4
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estimation_procedure : 50 times Clustering
Clustering on SalaryData
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estimation_procedure : 50 times Clustering
Clustering on iris
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estimation_procedure : 50 times Clustering
Clustering on dataset_time_8
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estimation_procedure : 50 times Clustering
Clustering on dataset_time_9
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estimation_procedure : 50 times Clustering
Clustering on dataset_time_10
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estimation_procedure : 50 times Clustering
Clustering on ricci_vs_destefano
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estimation_procedure : 50 times Clustering
Clustering on ZOO
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estimation_procedure : 50 times Clustering
Clustering on RelevantImagesDataset
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estimation_procedure : 50 times Clustering
Clustering on RelevantImagesDatasetTEST
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estimation_procedure : 50 times Clustering
Clustering on flags_new
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estimation_procedure : 50 times Clustering
Clustering on wingstop_stock_prices
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estimation_procedure : 50 times Clustering
Clustering on wingstop_stock_prices
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estimation_procedure : 50 times Clustering
Clustering on Brazilian_houses
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estimation_procedure : 50 times Clustering
Clustering on Brazilian_houses
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estimation_procedure : 50 times Clustering
Clustering on MY_DB
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estimation_procedure : 50 times Clustering
Clustering on weather
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estimation_procedure : 50 times Clustering
Clustering on weather
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estimation_procedure : 50 times Clustering
Clustering on test
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estimation_procedure : 50 times Clustering
Clustering on testdata
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estimation_procedure : 50 times Clustering
Clustering on testnew2
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estimation_procedure : 50 times Clustering
Clustering on test3
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estimation_procedure : 50 times Clustering
Clustering on iris
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estimation_procedure : 50 times Clustering
Clustering on iris
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estimation_procedure : 50 times Clustering
Clustering on Methane
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estimation_procedure : 50 times Clustering
Clustering on test4
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estimation_procedure : 50 times Clustering
Clustering on NEERI
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estimation_procedure : 50 times Clustering
Clustering on Yolanda
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estimation_procedure : 50 times Clustering
Clustering on Flora
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estimation_procedure : 50 times Clustering
Clustering on Flora
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estimation_procedure : 50 times Clustering
Clustering on shapes
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estimation_procedure : 50 times Clustering
Clustering on Bike_Sharing_Demand
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estimation_procedure : 50 times Clustering
Clustering on Bike_Sharing_Demand
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estimation_procedure : 50 times Clustering