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weka.SMO (1)
J. Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning,…
108 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…
70 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…
48 runs0 likes0 downloads0 reach0 impact
weka.classifiers.bayes.NaiveBayes (1)
George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
44 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors._classification.KNeighborsClassifier (1)
Classifier implementing the k-nearest neighbors vote.
34 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…
16 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (3)
A decision tree classifier.
16 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…
12 runs0 likes0 downloads0 reach4 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
weka.classifiers.meta.Bagging(weka.classifiers.meta.Bagging(weka.classifiers.meta.Bagging(weka.classifiers.functions.SMO(weka.classifiers.functions.supportVector.RBFKernel,weka.classifiers.functions.Logistic)))) (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
8 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.Bagging(weka.classifiers.meta.Bagging(weka.classifiers.functions.SMO(weka.classifiers.functions.supportVector.RBFKernel,weka.classifiers.functions.Logistic))) (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
8 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.Bagging(weka.classifiers.functions.SMO(weka.classifiers.functions.supportVector.RBFKernel,weka.classifiers.functions.Logistic)) (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
8 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.Bagging(weka.classifiers.trees.J48) (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
8 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors._classification.KNeighborsClassifier (2)
Classifier implementing the k-nearest neighbors vote.
6 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder),svc=sklearn.svm._classes.SVC) (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…
6 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=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…
6 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder),svc=sklearn.svm._classes.SVC) (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…
6 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder),svc=sklearn.svm._classes.SVC) (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…
6 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder),svc=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…
6 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=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…
6 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder),svc=sklearn.svm._classes.SVC) (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…
6 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.preprocessing._data.StandardScaler,cat=sklearn.preprocessing._encoders.OneHotEncoder),svc=sklearn.svm._classes.SVC) (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…
5 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.Bagging(weka.classifiers.meta.Bagging(weka.classifiers.trees.J48)) (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
4 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.trees.J48) (1)
Weka implementation.
4 runs0 likes0 downloads0 reach0 impact
weka.classifiers.trees.J48 (1)
Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
4 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.functions.SMO(weka.classifiers.functions.supportVector.RBFKernel,weka.classifiers.functions.Logistic))) (1)
Weka implementation.
4 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.Bagging(weka.classifiers.meta.Bagging(weka.classifiers.meta.Bagging(weka.classifiers.trees.J48))) (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
4 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.lazy.IBk(weka.core.neighboursearch.LinearNNSearch(weka.core.EuclideanDistance)))) (1)
Weka implementation.
4 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.functions.MultilayerPerceptron)) (1)
Weka implementation.
4 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.trees.RandomForest)) (1)
Weka implementation.
4 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.functions.Logistic)) (1)
Weka implementation.
4 runs0 likes0 downloads0 reach0 impact
weka.classifiers.trees.REPTree (1)
Weka implementation.
4 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.Bagging(weka.classifiers.meta.Bagging(weka.classifiers.meta.Bagging(weka.classifiers.functions.SMO(weka.classifiers.functions.supportVector.PolyKernel,weka.classifiers.functions.Logistic)))) (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
4 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.meta.LogitBoost(weka.classifiers.trees.REPTree))) (1)
Weka implementation.
4 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.MultiSearch(weka.classifiers.meta.multisearch.RandomSearch,weka.classifiers.meta.FilteredClassifier(weka.filters.MultiFilter(weka.filters.unsupervised.attribute.ReplaceMissingValues,weka.filters.unsupervised.attribute.RemoveUseless,weka.filters.unsupervised.attribute.Normalize),weka.classifiers.trees.J48)) (1)
Weka implementation.
4 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.FilteredClassifier(weka.filters.supervised.attribute.Discretize,weka.classifiers.trees.RandomForest) (1)
Weka implementation.
4 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.Bagging(weka.classifiers.meta.Bagging(weka.classifiers.functions.SMO(weka.classifiers.functions.supportVector.PolyKernel,weka.classifiers.functions.Logistic))) (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
4 runs0 likes0 downloads0 reach0 impact
sklearn.model_selection._search.GridSearchCV(estimator=sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier)) (6)
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "predict",…
4 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier) (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 fit…
4 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)) (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…
4 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (1)
A decision tree classifier.
3 runs0 likes0 downloads0 reach0 impact
weka.classifiers.meta.Bagging(weka.classifiers.functions.SMO(weka.classifiers.functions.supportVector.PolyKernel,weka.classifiers.functions.Logistic)) (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)) (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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(estimator=sklearn.tree._classes.DecisionTreeClassifier)) (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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)) (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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.tree._classes.DecisionTreeClassifier) (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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)) (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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,boosting=sklearn.ensemble._weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree._classes.DecisionTreeClassifier)) (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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(enc=sklearn.preprocessing._encoders.OneHotEncoder,rs=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)) (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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(enc=sklearn.preprocessing._encoders.OneHotEncoder,rs=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier)) (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…
3 runs0 likes0 downloads0 reach0 impact
TEST98795be233sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TEST10ee7d82d2sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TEST743572b543sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TEST33c4212ae0sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TEST802522d9d3sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TEST207c17e5ddsklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TEST55c2d9adb5sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TESTa8e496e7ebsklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TEST895b1cda38sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TESTad9814d53dsklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TEST2cfe6ce3absklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TESTb5a11472e5sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TESTfa1fb3c033sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TESTbc07589b97sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TESTb3850196c4sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TEST9f2999f04csklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TESTee4632d6f7sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TESTfca318b8acsklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TEST87b6f53d84sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TEST0965ca73d8sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TEST8aadbc93a3sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TESTacdb254685sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TEST4a20cce546sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TEST44efbafac9sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TESTe88a709c5csklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TEST74c4a4ef45sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TESTd9adb4a152sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TESTa5c87fb4b3sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TESTc510b76df3sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TEST1aa721112dsklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TEST18b6e8d9e1sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TESTea8856d651sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TEST086a08804esklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TEST00a3267939sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TESTcf5c0955e0sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TESTda307b21a4sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TESTb0b516031dsklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TEST3c0c63563asklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TESTa9ba7321a3sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TESTfcb1b11e91sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TESTffbeef0109sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TEST323265ebaesklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TEST41463f7f7fsklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TEST8b43678a01sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TEST719d657c45sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TESTb787efbdbdsklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TEST0023563da6sklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact
TEST0dd90f120esklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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…
2 runs0 likes0 downloads0 reach0 impact
TEST23a1e5aa0esklearn.pipeline.Pipeline(transformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(customimputer=openml.testing.CustomImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),classifier=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 fit…
2 runs0 likes0 downloads0 reach0 impact