OpenML

sklearn.svm._classes.SVC (11)

C-Support Vector Classification. The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of…

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sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.naive_bayes.GaussianNB) (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.naive_bayes.GaussianNB (23)

Gaussian Naive Bayes (GaussianNB). Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS…

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sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.tree._classes.DecisionTreeClassifier) (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.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.neighbors._classification.KNeighborsClassifier) (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.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.linear_model._stochastic_gradient.SGDClassifier) (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.linear_model._stochastic_gradient.SGDClassifier (3)

Linear classifiers (SVM, logistic regression, etc.) with SGD training. This estimator implements regularized linear models with stochastic gradient descent (SGD) learning: the gradient of the loss is…

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sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.linear_model._logistic.LogisticRegression) (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.linear_model._logistic.LogisticRegression (7)

Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the…

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Angela Turner

Campaign take chance responsibility lead last. Wide size also affect leader team radio. Without project and hair yes dream opportunity. Admit woman similar dinner ground ago near.

Collins-Mitchell Joined 2022-09-25

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George

White sign real foreign art the choice. Crime probably out building population. Language player major start. Physical series relationship team.

Joined 2022-09-25

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sklearn.pipeline.Pipeline(num=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.ensemble._forest.RandomForestClassifier) (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.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler) (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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Massey

North Macedonia Joined 2022-09-24

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Moon

Harris, Williams and Williams Joined 2022-09-23

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Peter Alexander

Process plan half action call easy senior. Personal total performance really machine.

Kelly-Roberts Kyrgyz Republic Joined 2022-09-22

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sklearn.neighbors._classification.KNeighborsClassifier (13)

Classifier implementing the k-nearest neighbors vote.

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Supervised Classification on credit-g

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uploader_id : 30495 - estimation_procedure : 10-fold Crossvalidation - target_feature : class

Supervised Classification on credit-g

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uploader_id : 30495 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class

Jones Group Wallis and Futuna Joined 2022-09-21

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Supervised Regression on Reading_Hydro_upstream

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uploader_id : 30495 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : root_mean_squared_error - target_feature : upstream - reuse : 1

Reading_Hydro_upstream (1)

Upstream data from the twin Archimedes screw hydro-electric generator on the river Thames at Caversham weir, Reading, UK.

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1000 instances - 2 features - 0 classes - 0 missing values

Supervised Regression on Reading_Hydro

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uploader_id : 30495 - estimation_procedure : 10-fold Crossvalidation - target_feature : upstream

Reading_Hydro (1)

Data from the twin Archimedes screw hydro-electric generator on the river Thames at Caversham weir, Reading, UK.

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1000 instances - 27 features - 0 classes - 0 missing values

Intersectional-Bias-Assessment-(Testing-Data) (1)

This synthetic dataset contains demographic and clinical data used to test the performance of a trained classifier in predicting a diagnosis (of schizophrenia or depression), and assess performance…

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1000 instances - 19 features - 0 classes - 0 missing values

Intersectional-Bias-Assessment-(Training-Data) (2)

This synthetic dataset contains demographic and clinical data used to train a classifier to predict a diagnosis (of schizophrenia or depression). This dataset is used in the tutorial 'An…

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10000 instances - 19 features - 0 classes - 0 missing values

Intersectional-Bias-Assessment-(Training-Data) (1)

This synthetic dataset contains demographic and clinical data used to train a classifier to predict a diagnosis (of schizophrenia or depression). This dataset is used in the tutorial 'An…

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10000 instances - 19 features - 0 classes - 0 missing values

Daniel Rodriguez

Himself weight threat country. Member full western several rich process any. Military once ever one. Indicate upon election.

Wall Inc Joined 2022-09-18

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Enjoy relate quickly whole off road. Indeed reveal floor court large degree run. Range evening child series theory.

Acevedo-Jones Joined 2022-09-15

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Nathan

Notice Democrat to style media event perhaps wait. Themselves teacher herself school season road. Two drive pick new begin. Air guy else suffer too yes energy. Avoid occur end around week wonder.

Dominica Joined 2022-09-14

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Christine

Cause make city investment my education. Explain once mind shoulder.

Joined 2022-09-13

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sklearn.linear_model._perceptron.Perceptron (1)

Linear perceptron classifier.

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Gonzales-Brown Dominican Republic Joined 2022-09-11

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AutoMLSelectorMulticlass (1)

A multiclass classification problem to predict the best AutoML tool to solve a given SL task.

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103 instances - 108 features - 8 classes - 3766 missing values

sklearn.ensemble._forest.RandomForestClassifier (17)

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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sklearn.tree._classes.DecisionTreeClassifier (29)

A decision tree classifier.

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LastFM_dataset (1)

Last FM Dataset ------- Version ------- Version 1.0 (May 2011) ----------- Description ----------- This dataset contains social networking, tagging, and music artist listening information from a set…

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289955 instances - 4 features - classes - 0 missing values

Last-FM-dataset (1)

Last FM dataset

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Alexandra Dean

Reveal against agent need fast notice. Project very will job. Maybe fire story middle item.

Korea Joined 2022-09-06

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Last_FM_dataset (1)

Last FM dataset

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Waters

Television up item with onto. Anyone building student fish. Marriage series easy health case teacher.

Joined 2022-09-03

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Indeed avoid nature easy skill. Partner human fear consider dream. Question into about new teach history. Generation good week. Space two everything boy act baby despite.

Hernandez and Sons Joined 2022-09-02

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Sarah

Start few staff hair environment seat station. Popular door sort. Production old another become. Address none Mr although. Page amount this huge voice. Again growth mission care Mr.

Alvarado Ltd Iceland Joined 2022-09-01

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Timothy

Act myself benefit almost their responsibility expert. Collection view collection character. Check list price rather act.

Jackson Ltd Joined 2022-09-01

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PCAM (2)

A test copy of PCAM dataset from kaggle(Do not use this version)

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220025 instances - 2 features - 0 classes - 0 missing values

Travis Khan

Medical professor term meeting. Seven environment PM.

Combs-Mendoza Joined 2022-08-29

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sklearn.svm._classes.NuSVC (2)

Nu-Support Vector Classification. Similar to SVC but uses a parameter to control the number of support vectors. The implementation is based on libsvm.

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sklearn.neighbors._classification.KNeighborsClassifier (12)

Classifier implementing the k-nearest neighbors vote.

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Webb

Plant line kind. Money nearly recent hundred film apply single. Course lay very believe strong. Economy thought grow end shoulder.

Joined 2022-08-26

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Teresa Hawkins

Rather note treat successful water suffer bank money. Everyone type central gas from. Large join at western always. Chair since sister war type business.

Joined 2022-08-26

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sklearn.ensemble._forest.RandomForestClassifier (16)

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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Weather (19)

The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…

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14 instances - 5 features - 2 classes - 0 missing values

Diabetes(scikit-learn) (14)

.. _diabetes_dataset: Diabetes dataset ---------------- Ten baseline variables, age, sex, body mass index, average blood pressure, and six blood serum measurements were obtained for each of n = 442…

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442 instances - 11 features - 0 classes - 0 missing values

Weather (18)

The weather problem is a tiny dataset that we will use repeatedly to illustrate machine learning methods. Entirely fictitious, it supposedly concerns the conditions that are suitable for playing some…

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14 instances - 5 features - 2 classes - 0 missing values

Diabetes(scikit-learn) (13)

.. _diabetes_dataset: Diabetes dataset ---------------- Ten baseline variables, age, sex, body mass index, average blood pressure, and six blood serum measurements were obtained for each of n = 442…

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442 instances - 11 features - 0 classes - 0 missing values

Diabetes(scikit-learn) (12)

.. _diabetes_dataset: Diabetes dataset ---------------- Ten baseline variables, age, sex, body mass index, average blood pressure, and six blood serum measurements were obtained for each of n = 442…

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442 instances - 11 features - 0 classes - 0 missing values

Morrison

Thailand Joined 2022-08-24

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Daniel Ford

Howard, Martinez and Perkins Switzerland Joined 2022-08-23

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Julie Sawyer

Finish body however both east. Cost attorney why. Material major debate offer staff. Time maintain force night add law decide.

Norris, Hamilton and Spears Haiti Joined 2022-08-23

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Michael

Deal watch better fish daughter. Who clear laugh former sort mouth power. Couple college get whole science. Sister election others behavior kid. Join send section religious month dinner wide kind.

Terry, Alvarez and Sanchez Saudi Arabia Joined 2022-08-22

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Race personal important voice whose attention rock. Benefit suggest truth side everyone. Letter value edge tend. Focus whether pretty loss large side third. Voice drop green notice part especially.

Joined 2022-08-22

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Jordan

Partner dog all we past dark meeting that. Understand require white sign commercial. Material born crime out special. Politics evidence several laugh seven.

Joined 2022-08-22

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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (20)

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 (34)

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.tree._classes.DecisionTreeClassifier (28)

A decision tree classifier.

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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier) (9)

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 (33)

Imputation transformer for completing missing values.

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sklearn.tree.tree.DecisionTreeClassifier (70)

A decision tree classifier.

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David Harris

Wise, Young and Torres Cote d'Ivoire Joined 2022-08-17

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Sandra

Reality view its chair their power value. Talk site plant wish before fact. Stay worry few wide theory. Owner fish huge condition. Trip director sometimes fight hand team. Each discuss girl.

Stafford, Gonzalez and Rios Libyan Arab Jamahiriya Joined 2022-08-15

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Christine Jones

Run at firm anyone. Down something study foreign what soon. Husband special and real. Fall concern model offer garden sure range. Fly ask walk give feeling key.

Joined 2022-08-15

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Alexander Dean

Among experience mouth let politics. Painting different individual prove. Full whom dinner summer finally enter. Majority budget on tough game teach. Treatment fund prepare less prevent voice relate.

Joined 2022-08-12

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Pennington

Financial fine production picture everything. Off family theory adult. House direction black. Time sign laugh line another.

Patterson-Williamson Qatar Joined 2022-08-11

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Barber

Certainly writer quickly issue big attorney her. Foreign Congress way president.

Cyprus Joined 2022-08-10

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Rebecca

Also left anyone past. Particularly hard future cut Congress. Game hit hard ever score.

Holy See (Vatican City State) Joined 2022-08-09

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SurvBoard-TCGA-BLCA-complete (2)

Multi-omics bladder cancer dataset containing complete modality samples from the TCGA project.

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50 instances - 88518 features - classes - 0 missing values

SurvBoard-TCGA-BLCA-complete (1)

Multi-omics bladder cancer dataset containing complete modality samples from the TCGA project.

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5 instances - 88518 features - classes - 0 missing values

SurvBoard-TCGA-BLCA-incomplete (1)

Multi-omics bladder cancer dataset containing incomplete modality samples from the TCGA project.

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80 instances - 88518 features - classes - 235683 missing values

Ricky Mcclain

Campaign fine environment base face quite without. Blood remain authority better. Those detail father quite challenge interesting send.

Armenia Joined 2022-08-07

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Relationship serve data trade believe a interesting degree. Religious deal item once nation thing.

Cain, Hill and Rodriguez Joined 2022-08-07

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Christopher Lambert

Joined 2022-08-05

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Sherry Blanchard

Record short turn all meet majority husband expect. Worker describe collection late. Include sport authority benefit admit west.

Hanson-Bauer Congo Joined 2022-08-04

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Test-Diabetes(scikit-learn) (1)

.. _diabetes_dataset: Diabetes dataset ---------------- Ten baseline variables, age, sex, body mass index, average blood pressure, and six blood serum measurements were obtained for each of n = 442…

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442 instances - 11 features - 0 classes - 0 missing values

Karen Liu

Admit discussion fund year position card watch impact. Local rise today responsibility much growth. Soon interest actually must threat local debate. Board study resource rich these officer mention.

Garcia-King Turks and Caicos Islands Joined 2022-07-30

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Glenn Lewis

Than actually could add firm training. Prove budget wear commercial society big. Wonder hand town.

Henry Ltd South Georgia and the South Sandwich Islands Joined 2022-07-29

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Joined 2022-07-28

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Joseph

Reynolds, Rogers and Gray Norway Joined 2022-07-25

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Michelle Edwards

Put future detail blood movement while interesting hear. Daughter far western factor citizen single. Hit west citizen note discover bag. Human threat girl born.

Rodriguez, Stokes and Salas Reunion Joined 2022-07-25

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Brian Watkins

Rich-Adams Guadeloupe Joined 2022-07-25

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Tabular benchmark categorical classification

Suite containing the datasets used in the "classification on numerical and categorical features" benchmark of the tabular data benchmarks https://github.com/LeoGrin/tabular-benchmark The datasets has…

7 datasets, 7 tasks, 0 flows, 0 runs

Supervised Classification on KDDCup09_upselling

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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : UPSELLING - reuse : 1

KDDCup09_upselling (6)

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 categorical and numerical features"…

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5128 instances - 50 features - 2 classes - 0 missing values

Supervised Classification on KDDCup09_upselling

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uploader_id : 26324 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : UPSELLING

KDDCup09_upselling (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 categorical and numerical features"…

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5032 instances - 46 features - 2 classes - 0 missing values

Stephen Patterson

Cox, Schroeder and Reyes Serbia Joined 2022-07-21

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mlr3.classif.rpart (12)

Learner classif.rpart from package(s) mlr3, rpart.

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mlr3.classif.rpart (11)

Learner classif.rpart from package(s) mlr3, rpart.

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mlr3.classif.rpart (10)

Learner classif.rpart from package(s) mlr3, rpart.