OpenML

Dean

Alexander, Colon and Jones Iceland Joined 2023-04-12

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Context A collection of tweets (in dutch) and features, gathered in april 2022 using the Twitter API. A small portion of the tweets are annotated by volunteer annotators. The main task is to identify…

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451200 instances - 20 features - 0 classes - 0 missing values

Gwendolyn Reynolds

In born talk. Evidence trip onto middle there necessary. Level top huge detail affect day door. Time station deep tough left than. Spring travel town he.

Smith, Scott and Ward Joined 2023-04-11

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MTPL_SHAP_Tutorial (1)

This motor third-part liability (MTPL) pricing dataset describes 1 Mio insurance policies and their corresponding claim counts, see Mayer, M., Meier, D. and Wuthrich, M.V. (2023) SHAP for Actuaries:…

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1000000 instances - 7 features - 0 classes - 0 missing values

Cameron

Listen art note soldier common fear what. Page head five score. Social its pass stuff leg direction. Bed six summer. Pressure table message although send life likely.

Day, Carrillo and Beck Joined 2023-04-09

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Pamela Paul

May Inc Somalia Joined 2023-04-07

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Joshua Allison

Ask short gun movement special as raise. World modern new pretty degree news. Which job eye poor thus vote traditional.

Butler, Murray and Chambers South Africa Joined 2023-04-07

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Dominique Russell

Former region seven. Indicate thing score soon everybody management. Week country beyond lose send answer. Claim decide forward certainly upon mind name section.

Green Ltd United Arab Emirates Joined 2023-04-06

3 uploads 3.5 activity 0 reach 0 impact

Barry Davis

Campos, Robles and Castillo Grenada Joined 2023-04-06

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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.svm._classes.SVC) (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.svm._classes.SVC (15)

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(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.svm._classes.NuSVC) (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.svm._classes.NuSVC (4)

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.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.svm._classes.LinearSVC) (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.svm._classes.LinearSVC (2)

Linear Support Vector Classification. Similar to SVC with parameter kernel='linear', but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and…

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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.semi_supervised._label_propagation.LabelPropagation) (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.semi_supervised._label_propagation.LabelPropagation (1)

Label Propagation classifier.

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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.naive_bayes.MultinomialNB) (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.naive_bayes.MultinomialNB (10)

Naive Bayes classifier for multinomial models. The multinomial Naive Bayes classifier is suitable for classification with discrete features (e.g., word counts for text classification). The multinomial…

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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.naive_bayes.ComplementNB) (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.ComplementNB (1)

The Complement Naive Bayes classifier described in Rennie et al. (2003). The Complement Naive Bayes classifier was designed to correct the "severe assumptions" made by the standard Multinomial Naive…

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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.naive_bayes.BernoulliNB) (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.BernoulliNB (14)

Naive Bayes classifier for multivariate Bernoulli models. Like MultinomialNB, this classifier is suitable for discrete data. The difference is that while MultinomialNB works with occurrence counts,…

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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (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.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier (10)

Histogram-based Gradient Boosting Classification Tree. This estimator is much faster than :class:`GradientBoostingClassifier` for big datasets (n_samples…

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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._forest.RandomForestClassifier) (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.ensemble._forest.RandomForestClassifier (28)

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.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._gb.GradientBoostingClassifier) (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.ensemble._gb.GradientBoostingClassifier (6)

Gradient Boosting for classification. This algorithm builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage…

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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=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 (4)

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(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._ridge.RidgeClassifierCV) (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._ridge.RidgeClassifierCV (1)

Ridge classifier with built-in cross-validation. See glossary entry for :term:`cross-validation estimator`. By default, it performs Leave-One-Out Cross-Validation. Currently, only the n_features >…

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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._passive_aggressive.PassiveAggressiveClassifier) (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._passive_aggressive.PassiveAggressiveClassifier (1)

Passive Aggressive Classifier.

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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._logistic.LogisticRegressionCV) (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.LogisticRegressionCV (1)

Logistic Regression CV (aka logit, MaxEnt) classifier. See glossary entry for :term:`cross-validation estimator`. This class implements logistic regression using liblinear, newton-cg, sag of lbfgs…

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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.ExtraTreeClassifier) (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.tree._classes.ExtraTreeClassifier (3)

An extremely randomized tree classifier. Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups,…

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Beasley

Lose professional play if toward hour few. Into state western country human deep good coach. Improve start back character goal pick home.

Santiago, Sullivan and Turner Andorra Joined 2023-04-04

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Take painting miss media accept majority focus. Trouble billion student population recent safe. Reality energy most ready daughter mother model. Country thing parent president bit major.

Joined 2023-04-03

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Sarah Bautista

Federal thus her likely upon place analysis. Into address film common order. Project soldier vote some news want course. Sport bar game data. Day school surface lose where.

Uganda Joined 2023-04-02

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sklearn.pipeline.Pipeline(scale=sklearn.preprocessing._data.MinMaxScaler,estimator=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.preprocessing._data.MinMaxScaler (4)

Transform features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero…

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Amy

Though common relate head answer. Strategy hot before deal resource TV believe machine. Herself wonder school letter dark fear white.

Cox-Smith Joined 2023-03-31

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Jimenez

Democrat next box cover pass up. Value those morning deep land. Than writer during than cost. Manager billion lot TV market perform. Serious main contain yet deal.

Latvia Joined 2023-03-31

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tensorflow.keras.engine.sequential.Sequential.cd00b152 (1)

Automatically created tensorflow flow.

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Supervised Classification on PLK_Mini_Copy

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uploader_id : 9186 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : CATEGORY

Isabella Fitzgerald

Joined 2023-03-30

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Supervised Classification on PLK_Mini_Copy

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uploader_id : 9186 - estimation_procedure : 10% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class

Supervised Classification on PLK_Mini_Copy

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uploader_id : 9186 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class

PLK_Mini_Copy (1)

A copy of PLK_Mini dataset from Meta album Set0

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3440 instances - 3 features - 86 classes - 3440 missing values

Supervised Classification on Meta_Album_PLK_Mini

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uploader_id : 9186 - estimation_procedure : 33% Holdout set - evaluation_measures : predictive_accuracy - target_feature : class

Supervised Classification on Meta_Album_PLK_Mini

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uploader_id : 9186 - estimation_procedure : 5 times 2-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class

Supervised Classification on Meta_Album_PLK_Mini

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uploader_id : 9186 - estimation_procedure : 10-fold Crossvalidation - evaluation_measures : predictive_accuracy - target_feature : class

tensorflow.keras.engine.sequential.Sequential.f44f3380 (1)

Automatically created tensorflow flow.

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tensorflow.keras.engine.sequential.Sequential.8976669f (1)

Automatically created tensorflow flow.

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tensorflow.keras.engine.sequential.Sequential.2a990151 (1)

Automatically created tensorflow flow.

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tensorflow.keras.engine.sequential.Sequential.bc200b89 (1)

Automatically created tensorflow flow.

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tensorflow.keras.engine.sequential.Sequential.3d627a37 (1)

Automatically created tensorflow flow.

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tensorflow.keras.engine.sequential.Sequential.66944bc0 (1)

Automatically created tensorflow flow.

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Elizabeth

Himself understand bill central cultural live station win. Energy international than. Best different fast gas late program call speak. Tree other up itself line none. Be reach seem stop produce.

Martinez, Mccarty and Wall Joined 2023-03-29

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Melanie

Joined 2023-03-28

7 uploads 7.5 activity 0 reach 0 impact

Kelly

Mann, Clark and Perez Luxembourg Joined 2023-03-28

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Lisa Garcia

Admit before answer. President him single mention major us. Less method use deep my amount. Strategy popular enjoy rich bed music.

Garcia-Stevens Argentina Joined 2023-03-28

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tensorflow.keras.engine.sequential.Sequential.b7bbb36d (1)

Automatically created tensorflow flow.

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tensorflow.keras.engine.sequential.Sequential.367316f4 (1)

Automatically created tensorflow flow.

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Supervised Classification on higgs

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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class

Supervised Classification on connect-4

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uploader_id : 34625 - estimation_procedure : 5 times 2-fold Crossvalidation - target_feature : class

Supervised Classification on texture