OpenML

A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…

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sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier) (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…

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torch.nn.Model2.a524e7d6f2f5accd (1)

Automatically created pytorch flow.

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sklearn.ensemble._forest.RandomForestClassifier (44)

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.tree._classes.DecisionTreeClassifier) (33)

A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…

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tensorflow.keras.src.engine.sequential.Sequential.faf20af1 (1)

Automatically created tensorflow flow.

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sklearn.pipeline.Pipeline(preprocessor=pairwise.padre.PDCDataTransformer,classifier=pairwise.padre.PairwiseDifferenceClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier)) (1)

A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…

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torch.nn.Net.2ffa3de8228eefa8 (1)

Automatically created pytorch flow.

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torch.nn.Net.33d5d8519eae7da2 (1)

Automatically created pytorch flow.

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torch.nn.Net.1c39b0d3ec8abb7 (1)

Automatically created pytorch flow.

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torch.nn.Net.5a03bdc898bc4471 (1)

Automatically created pytorch flow.

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torch.nn.Net.9697466e1fc2753c (1)

Automatically created pytorch flow.

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torch.nn.Net.9c4d970e533385de (1)

Automatically created pytorch flow.

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torch.nn.Model2.8bf0bed05e15174a (1)

Automatically created pytorch flow.

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torch.nn.Net.96431ee7ff91cc1b (1)

Automatically created pytorch flow.

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torch.nn.Net.32cd490fe404147 (1)

Automatically created pytorch flow.

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torch.nn.Model2.f6f4825e90808d59 (1)

Automatically created pytorch flow.

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sklearn.ensemble._forest.RandomForestClassifier (46)

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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torch.nn.Model2.adcabb16721694f4 (1)

Automatically created pytorch flow.

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torch.nn.Net.af3b4d7761ef7af (1)

Automatically created pytorch flow.

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torch.nn.TabularClassificationmodel.32873d53d0202561 (1)

Automatically created pytorch flow.

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torch.nn.ResNet.2a12408ac25b016a (1)

Automatically created pytorch flow.

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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (34)

A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…

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torch.nn.TabularClassificationmodel.481f5d46717292fa (1)

Automatically created pytorch flow.

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torch.nn.Sequential.bb8de941c9933dd2 (1)

Automatically created pytorch flow.

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tensorflow.keras.src.engine.sequential.Sequential.ab990de8 (2)

Automatically created tensorflow flow.

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sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.ensemble._forest.RandomForestClassifier) (1)

A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…

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sklearn.ensemble._forest.RandomForestClassifier (48)

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(preprocessing=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler),classifier=sklearn.svm._classes.SVC) (1)

A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…

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sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.svm._classes.SVC) (1)

A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…

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sklearn.pipeline.Pipeline(classifier=sklearn.svm._classes.SVC) (1)

A sequence of data transformers with an optional final predictor. `Pipeline` allows you to sequentially apply a list of transformers to preprocess the data and, if desired, conclude the sequence with…

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sklearn.ensemble._forest.RandomForestClassifier (49)

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.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier) (1)

An AdaBoost classifier. An AdaBoost [1]_ classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same…

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sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier) (2)

An AdaBoost classifier. An AdaBoost [1]_ classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same…

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