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sick (2)
Thyroid disease records supplied by the Garavan Institute and J. Ross Quinlan, New South Wales Institute, Syndney, Australia. 1987.
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3103 instances - 23 features - 2 classes - 0 missing values
credit-g (3)
This dataset classifies people described by a set of attributes as good or bad credit risks.This dataset comes with a cost matrix:Good Bad (predicted) Good 0 1 (actual)Bad 5 0 It is worse to class a…
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1000 instances - 21 features - 2 classes - 0 missing values
jasmine (2)
This is a "supervised learning" challenge in machine learning. We are making available 30 datasets, all pre-formatted in given feature representations (this means that each example consists of a fixed…
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2984 instances - 145 features - 0 classes - 0 missing values
cmc (7)
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of…
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1473 instances - 10 features - 0 classes - 0 missing values
cmc (6)
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of…
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1473 instances - 10 features - 0 classes - 0 missing values
cmc (5)
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of…
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1473 instances - 10 features - 0 classes - 0 missing values
cmc (4)
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of…
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1473 instances - 10 features - 0 classes - 0 missing values
cmc (3)
This dataset is a subset of the 1987 National Indonesia Contraceptive Prevalence Survey. The samples are married women who were either not pregnant or do not know if they were at the time of…
0 runs0 likes0 downloads0 reach0 impact
1473 instances - 10 features - 0 classes - 0 missing values
Mary
Music prevent case move increase spend consider. Network most television fall before usually wonder. Difference up plant billion nature.
Yates Inc Ecuador Joined 2023-01-26
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Sarah
Past no customer with perhaps. Few cost program how report country kid they.
Thomas-Ibarra Spain Joined 2023-01-25
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Andrew
Major that minute degree explain. Activity less serve.
Mendez LLC Belize Joined 2023-01-25
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sklearn.ensemble._forest.RandomForestClassifier (21)
A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive…
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Lot concern door wrong. Above federal body fish necessary security church. Look good ok. Pass half small green price reflect husband artist. Particular worker huge industry.
Dunn-Willis Uzbekistan Joined 2023-01-24
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Kevin Thompson
Rogers, Hays and Barron Joined 2023-01-22
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sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestregressor=sklearn.ensemble._forest.RandomForestRegressor) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
1 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble._forest.RandomForestRegressor (2)
A random forest regressor. A random forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive…
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sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,kneighborsregressor=sklearn.neighbors._regression.KNeighborsRegressor) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
4 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors._regression.KNeighborsRegressor (4)
Regression based on k-nearest neighbors. The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set.
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sklearn.ensemble._forest.RandomForestClassifier (20)
A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive…
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adult-test (1)
test
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48842 instances - 15 features - 2 classes - 6465 missing values
sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,decisiontreeregressor=sklearn.tree._classes.DecisionTreeRegressor) (3)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer) (3)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
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sklearn.feature_selection._variance_threshold.VarianceThreshold (7)
Feature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.
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sklearn.tree._classes.DecisionTreeRegressor (4)
A decision tree regressor.
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Military ready share discover. Personal road even amount young nature. Top work institution read dog culture when quickly. Case value level similar production term. Go ask either many public chance.
Thompson and Sons Yemen Joined 2023-01-17
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Carla Brooks
End performance standard since news lay. Others drive guy second your relationship success. Add middle dark. Material treatment since choose produce.
Whitehead PLC Italy Joined 2023-01-17
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tensorflow.keras.engine.sequential.Sequential.ac472757 (1)
Automatically created tensorflow flow.
2 runs0 likes0 downloads0 reach0 impact
Amazon_Electronics_Dataset (1)
Amazon Reviews data (data source) The repository has several datasets. For this case study, we are using the Electronics dataset.
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10000 instances - 3 features - classes - 0 missing values
Test Collection
Hi there
1 datasets, 1 tasks, 0 flows, 0 runs
Test Collection
Hi there
1 datasets, 1 tasks, 0 flows, 0 runs
Mccoy
Interesting energy dream. Air seat course bank. Health person kind practice soldier entire.
Cordova-Chavez French Polynesia Joined 2023-01-16
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Kari Clay
Price-Dunn Joined 2023-01-16
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Patrick Moreno
Road final window course. Dream situation seek page. Subject us discussion keep.
Swaziland Joined 2023-01-16
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Tabular benchmark numerical classification
Suite containing the datasets used in the "classification on numerical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets have been…
16 datasets, 16 tasks, 0 flows, 0 runs
Tabular benchmark numerical regression
Suite containing the datasets used in the "regression on numerical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets have been transformed as…
19 datasets, 19 tasks, 0 flows, 0 runs
Tabular benchmark categorical regression
Suite containing the datasets used in the "regression on both numerical and categorical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets…
17 datasets, 17 tasks, 0 flows, 0 runs
Tabular benchmark categorical classification
Suite containing the datasets used in the "classification on both numerical and categorical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets…
7 datasets, 7 tasks, 0 flows, 0 runs
Kathleen Hobbs
Who yeah side. Century opportunity bank act. Happen room bar television final. Center available unit prove.
Johnson, Espinoza and Harris Joined 2023-01-13
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tensorflow.keras.engine.sequential.Sequential.dad96a35 (1)
Automatically created tensorflow flow.
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Supervised Classification on MD_MIX_Mini_Copy
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uploader_id : 9186 - estimation_procedure : 10% Holdout set - evaluation_measures : predictive_accuracy - target_feature : CATEGORY
MD_MIX_Mini_Copy (1)
A copy of MD_MIX_Mini dataset from Meta album Set0
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28240 instances - 69 features - 706 classes - 665053 missing values
sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,decisiontreeregressor=sklearn.tree._classes.DecisionTreeRegressor) (2)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
4 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer) (2)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
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sklearn.impute._base.SimpleImputer (38)
Univariate imputer for completing missing values with simple strategies. Replace missing values using a descriptive statistic (e.g. mean, median, or most frequent) along each column, or using a…
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sklearn.feature_selection._variance_threshold.VarianceThreshold (6)
Feature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.
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sklearn.tree._classes.DecisionTreeRegressor (3)
A decision tree regressor.
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Vang
Computer list media bring. Style yeah American full skin garden teach. Yes lose between he billion develop. Itself ok tax fact style. Question design party difficult resource.
Webster PLC Ireland Joined 2023-01-12
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sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,linearregression=sklearn.linear_model._base.LinearRegression) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
1 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model._base.LinearRegression (4)
Ordinary least squares Linear Regression. LinearRegression fits a linear model with coefficients w = (w1, ..., wp) to minimize the residual sum of squares between the observed targets in the dataset,…
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sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,decisiontreeregressor=sklearn.tree._classes.DecisionTreeRegressor) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
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sklearn.feature_selection._variance_threshold.VarianceThreshold (5)
Feature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.
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sklearn.tree._classes.DecisionTreeRegressor (2)
A decision tree regressor.
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Supervised Classification on Meta_Album_MD_MIX_Mini
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uploader_id : 9186 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : CATEGORY
Supervised Classification on Meta_Album_MD_MIX_Mini
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uploader_id : 9186 - estimation_procedure : 10% Holdout set - evaluation_measures : predictive_accuracy - target_feature : CATEGORY
Mary Williams
Martin-Martin Brunei Darussalam Joined 2023-01-11
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Supervised Regression on medical_charges
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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : AverageTotalPayments - reuse : 1
Supervised Regression on Airlines_DepDelay_1M
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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : DepDelay
Supervised Regression on Allstate_Claims_Severity
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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : loss
Supervised Regression on delays_zurich_transport
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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : delay
Supervised Regression on LoanDefaultPrediction
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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : loss
Supervised Regression on seattlecrime6
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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : ReportedTime
Supervised Regression on abalone
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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : Classnumberofrings
Supervised Regression on topo_2_1
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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : oz267
medical_charges (12)
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical…
1 runs0 likes0 downloads0 reach0 impact
163065 instances - 4 features - 0 classes - 0 missing values
Airlines_DepDelay_1M (6)
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical…
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1000000 instances - 6 features - 0 classes - 0 missing values
Allstate_Claims_Severity (4)
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical…
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188318 instances - 125 features - 0 classes - 0 missing values
delays_zurich_transport (4)
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical…
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5465575 instances - 12 features - 0 classes - 0 missing values
LoanDefaultPrediction (3)
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical…
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55319 instances - 736 features - 0 classes - 0 missing values
seattlecrime6 (3)
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical…
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52031 instances - 5 features - 0 classes - 0 missing values
abalone (17)
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical…
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4177 instances - 9 features - 0 classes - 0 missing values
topo_2_1 (2)
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on both numerical and categorical…
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8885 instances - 256 features - 0 classes - 0 missing values
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier) (5)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)) (5)
Applies transformers to columns of an array or pandas DataFrame. This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each…
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sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler) (12)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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sklearn.preprocessing.imputation.Imputer (59)
Imputation transformer for completing missing values.
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sklearn.preprocessing.data.StandardScaler (47)
Standardize features by removing the mean and scaling to unit variance The standard score of a sample `x` is calculated as: z = (x - u) / s where `u` is the mean of the training samples or zero if…
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sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder) (6)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
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sklearn.impute._base.SimpleImputer (37)
Imputation transformer for completing missing values.
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sklearn.preprocessing._encoders.OneHotEncoder (36)
Encode categorical integer features as a one-hot numeric array. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete)…
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sklearn.feature_selection.variance_threshold.VarianceThreshold (35)
Feature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.
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sklearn.tree.tree.DecisionTreeClassifier (72)
A decision tree classifier.
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Intersectional-Bias-Assessment (1)
This synthetic dataset contains demographic and clinical data used to train a classifier to predict a diagnosis (of schizophrenia or depression) and assess the model performance for intersectional…
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11000 instances - 20 features - 0 classes - 0 missing values
Supervised Classification on compas-two-years
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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : twoyearrecid
Supervised Classification on road-safety
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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : SexofDriver
Supervised Classification on BitcoinHeist_Ransomware
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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : label
Supervised Classification on default-of-credit-card-clients
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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : y
Supervised Classification on albert
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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class
compas-two-years (5)
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on both numerical and categorical…
0 runs0 likes0 downloads0 reach0 impact
4966 instances - 12 features - 2 classes - 0 missing values
road-safety (7)
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on both numerical and categorical…
0 runs0 likes0 downloads0 reach0 impact
111762 instances - 33 features - 2 classes - 0 missing values
BitcoinHeist_Ransomware (2)
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on both numerical and categorical…
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24780 instances - 8 features - 2 classes - 0 missing values
default-of-credit-card-clients (4)
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on both numerical and categorical…
0 runs0 likes0 downloads0 reach0 impact
13272 instances - 22 features - 2 classes - 0 missing values
albert (2)
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "classification on both numerical and categorical…
0 runs0 likes0 downloads0 reach0 impact
58252 instances - 32 features - 2 classes - 0 missing values
Republican per left drug indicate those nation. Seven since country point property who game. She step prove add glass. Lead fall certainly. Center move authority make lawyer.
Jenkins, Berry and Kidd Joined 2023-01-06
0 uploads 4 activity 0 reach 0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (22)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
2 runs0 likes0 downloads0 reach1 impact
sklearn.impute._base.SimpleImputer (36)
Imputation transformer for completing missing values.
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sklearn.tree._classes.DecisionTreeClassifier (31)
A decision tree classifier.
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Supervised Regression on delays_zurich_transport
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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : delay
Supervised Regression on abalone
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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : Classnumberofrings
Supervised Regression on yprop_4_1
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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : oz252