DEVELOPMENT... OpenML
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John Ross
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John's flows

flow
sklearn.ensemble._forest.RandomForestClassifier (6)
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…
2 runs0 likes0 downloads0 reach1 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,logisticregression=sklearn.linear_model.logistic.LogisticRegression) (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 fit…
2645 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),logisticregression=sklearn.linear_model.logistic.LogisticRegression) (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 fit…
3550 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),logisticregression=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 fit…
34 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)) (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(standardscaler=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 fit…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder) (8)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,logisticregression=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression) (2)
Automatically created scikit-learn flow.
84614 runs0 likes1 downloads1 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),logisticregression=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,logisticregression=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 fit…
1929 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing._encoders.OneHotEncoder (21)
Encode categorical 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) features.…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler,logisticregression=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 fit…
8 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing._data.StandardScaler (4)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model._logistic.LogisticRegression (3)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=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 fit…
1278 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (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 fit…
22 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=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 fit…
17 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC) (4)
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…
29 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),svc=sklearn.svm.classes.SVC) (4)
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…
228 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)) (6)
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(standardscaler=sklearn.preprocessing.data.StandardScaler) (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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder) (7)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (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 fit…
1520 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (64)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),randomforestclassifier=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 fit…
133 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)) (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(standardscaler=sklearn.preprocessing._data.StandardScaler) (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 fit…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=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 fit…
170 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble._forest.RandomForestClassifier (2)
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…
2 runs0 likes0 downloads0 reach2 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (2)
Automatically created scikit-learn flow.
9626 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
2829 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1643 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (63)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=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 fit…
99 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing.data.StandardScaler (39)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (62)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC) (4)
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…
558 runs0 likes0 downloads0 reach0 impact
sklearn.svm.classes.SVC (40)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC) (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 fit…
1 runs0 likes1 downloads1 reach0 impact
sklearn.preprocessing._encoders.OneHotEncoder (18)
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)…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC) (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 fit…
6 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing.data.StandardScaler (37)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(svc=sklearn.svm.classes.SVC) (4)
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…
2 runs0 likes0 downloads0 reach0 impact
sklearn.svm.classes.SVC (37)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),svc=sklearn.svm.classes.SVC) (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 fit…
2 runs0 likes0 downloads0 reach0 impact
sklearn.impute._base.SimpleImputer (7)
Imputation transformer for completing missing values.
0 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)) (4)
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(standardscaler=sklearn.preprocessing.data.StandardScaler) (4)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing.data.StandardScaler (33)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder) (4)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing._encoders.OneHotEncoder (14)
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)…
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(standardscaler=sklearn.preprocessing.data.StandardScaler,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
4 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(standardscaler=sklearn.preprocessing.data.StandardScaler,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(svc=sklearn.svm.classes.SVC) (3)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC) (4)
Automatically created scikit-learn flow.
6 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),svc=sklearn.svm.classes.SVC) (2)
Automatically created scikit-learn flow.
6877 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)) (2)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler) (2)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder) (2)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
16 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC) (2)
Automatically created scikit-learn flow.
967 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneHotEncoder)),svc=sklearn.svm.classes.SVC) (2)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneHotEncoder)) (2)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler) (2)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneHotEncoder) (2)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing._encoders.OneHotEncoder (12)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC) (2)
Automatically created scikit-learn flow.
167 runs0 likes0 downloads0 reach0 impact
sklearn.impute._base.SimpleImputer (3)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing.data.StandardScaler (30)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.svm.classes.SVC (32)
Automatically created scikit-learn flow.
17317 runs0 likes0 downloads0 reach17317 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneHotEncoder)),svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
2 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneHotEncoder)) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneHotEncoder) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
12377 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing._encoders.OneHotEncoder (11)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkceigenpro=sklearn_extra.fast_kernel.FKCEigenPro) (1)
Automatically created scikit-learn flow.
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sklearn_extra.fast_kernel.FKCEigenPro (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,fkc_eigenpro=sklearn_extra.fast_kernel.FKC_EigenPro) (1)
Automatically created scikit-learn flow.
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sklearn.preprocessing.data.StandardScaler (29)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,fkc_eigenpro=sklearn_extra.fast_kernel.FKC_EigenPro) (1)
Automatically created scikit-learn flow.
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sklearn_extra.fast_kernel.FKC_EigenPro (1)
Automatically created scikit-learn flow.
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sklearn.linear_model.logistic.LogisticRegression (26)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(feature_map=sklearn_extra.kernel_approximation._fastfood.Fastfood,svm=sklearn.svm.classes.LinearSVC) (1)
Automatically created scikit-learn flow.
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sklearn_extra.kernel_approximation._fastfood.Fastfood (1)
Automatically created scikit-learn flow.
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sklearn.svm.classes.LinearSVC (10)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,histgradientboostingclassifier=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.impute._base.SimpleImputer (1)
Automatically created scikit-learn flow.
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sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier (2)
Automatically created scikit-learn flow.
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