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sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer) (2)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(transform=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer),estimator=sklearn.ensemble._forest.RandomForestClassifier) (2)
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…
2 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier) (2)
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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer) (2)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD) (2)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.decomposition._truncated_svd.TruncatedSVD (2)
Dimensionality reduction using truncated SVD (aka LSA). This transformer performs linear dimensionality reduction by means of truncated singular value decomposition (SVD). Contrary to PCA, this…
0 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer) (1)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD) (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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.decomposition._truncated_svd.TruncatedSVD (1)
Dimensionality reduction using truncated SVD (aka LSA). This transformer performs linear dimensionality reduction by means of truncated singular value decomposition (SVD). Contrary to PCA, this…
0 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer) (1)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors._classification.KNeighborsClassifier (2)
Classifier implementing the k-nearest neighbors vote.
7 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors._classification.KNeighborsClassifier (3)
Classifier implementing the k-nearest neighbors vote.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=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…
72 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(transform=sklearn.compose._column_transformer.ColumnTransformer(cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,truncatedsvd=sklearn.decomposition._truncated_svd.TruncatedSVD),cont=sklearn.impute._base.SimpleImputer),estimator=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…
24 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble._forest.RandomForestClassifier (1)
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…
102 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors._classification.KNeighborsClassifier (1)
Classifier implementing the k-nearest neighbors vote.
50 runs0 likes0 downloads0 reach0 impact
amlb_constantpredictor (1)
This flow is generated by the automl benchmark: https://github.com/openml/automlbenchmark.git Repository commit: 51e692e76e4fa6b6981cccdeee7769f2bd8eded1 constantpredictor version: 1.2.2
4 runs0 likes0 downloads0 reach0 impact
amlb_H2OAutoML (1)
This flow is generated by the automl benchmark: https://github.com/openml/automlbenchmark.git Repository commit: 482b6e6b61258f3ace36d91a4a72756ac45b61e5 H2OAutoML version: stable
4 runs0 likes0 downloads0 reach0 impact