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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.MinMaxScaler,step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis) (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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.linear_model._stochastic_gradient.SGDClassifier),step_2=sklearn.svm._classes.SVC) (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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.feature_selection._univariate_selection.SelectKBest,step_2=sklearn.linear_model._stochastic_gradient.SGDClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier),step_2=sklearn.linear_model._stochastic_gradient.SGDClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.MinMaxScaler,step_2=sklearn.naive_bayes.MultinomialNB) (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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.discriminant_analysis.LinearDiscriminantAnalysis),step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.svm._classes.SVC),step_2=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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._pca.PCA,step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier) (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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.svm._classes.SVC),step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.MinMaxScaler,step_2=sklearn.naive_bayes.BernoulliNB) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier),step_2=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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._fastica.FastICA,step_2=sklearn.naive_bayes.BernoulliNB) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.MinMaxScaler,step_2=sklearn.naive_bayes.BernoulliNB) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.MaxAbsScaler,step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier) (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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.MaxAbsScaler,step_2=sklearn.naive_bayes.BernoulliNB) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.linear_model._stochastic_gradient.SGDClassifier),step_2=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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier),step_2=sklearn.linear_model._stochastic_gradient.SGDClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier),step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis) (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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._discretization.KBinsDiscretizer,step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier) (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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.discriminant_analysis.LinearDiscriminantAnalysis),step_2=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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.feature_selection._univariate_selection.SelectKBest,step_2=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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.Normalizer,step_2=sklearn.naive_bayes.BernoulliNB) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.feature_selection._univariate_selection.SelectKBest,step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis) (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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),step_2=sklearn.svm._classes.SVC) (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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.feature_selection._univariate_selection.SelectKBest,step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier) (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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.PolynomialFeatures,step_2=sklearn.naive_bayes.BernoulliNB) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.MaxAbsScaler,step_2=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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.svm._classes.SVC),step_2=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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._pca.PCA,step_2=sklearn.svm._classes.SVC) (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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectKBest,step_1=sklearn.svm._classes.SVC) (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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.StandardScaler,step_2=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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.PolynomialFeatures,step_2=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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.QuantileTransformer,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.MaxAbsScaler,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.MinMaxScaler,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_2=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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_2=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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.StandardScaler,step_2=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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.PolynomialFeatures,step_2=sklearn.svm._classes.SVC) (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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.feature_selection._variance_threshold.VarianceThreshold,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.feature_selection._univariate_selection.SelectKBest,step_2=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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.impute._base.MissingIndicator,step_2=sklearn.svm._classes.SVC) (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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.feature_selection._variance_threshold.VarianceThreshold,step_2=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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
1 runs0 likes0 downloads0 reach0 impact
mxnet.gluon.nn.basic_layers.HybridSequential.aeee695 (1)
Automatically created MXNet flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.svm.classes.NuSVC (3)
Nu-Support Vector Classification. Similar to SVC but uses a parameter to control the number of support vectors. The implementation is based on libsvm.
1 runs0 likes0 downloads0 reach0 impact
keras.engine.sequential.Sequential.3aeb2942 (1)
Automatically created keras flow.
1 runs0 likes0 downloads0 reach0 impact
keras.engine.sequential.Sequential.6fe33635 (1)
Automatically created keras flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors._classification.KNeighborsClassifier (4)
Classifier implementing the k-nearest neighbors vote.
1 runs0 likes0 downloads0 reach0 impact
torch.nn.modules.container.Sequential.ee39b87dd683a3ec (1)
Automatically created pytorch flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,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 fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model._logistic.LogisticRegression (4)
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…
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sklearn.ensemble._forest.RandomForestClassifier (5)
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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mlr.classif.rpart (60)
Learner mlr.classif.rpart from package(s) rpart.
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mlr.classif.rpart.tuned (18)
Learner mlr.classif.rpart.tuned from package(s) rpart.
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sklearn.pipeline.Pipeline(kneighborsclassifier=sklearn.neighbors._classification.KNeighborsClassifier) (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…
1 runs0 likes0 downloads0 reach0 impact
mango.model_selection.search.MangoSearchCV(estimator=sklearn.linear_model._logistic.LogisticRegression) (1)
Mango Search CV
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,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 fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=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 fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (10)
A decision tree classifier.
1 runs0 likes0 downloads0 reach1 impact
keras.engine.sequential.Sequential.634a76ba (1)
Automatically created keras flow.
1 runs0 likes0 downloads0 reach0 impact
amlb_constantpredictor (1)
This flow is generated by the automl benchmark: https://github.com/openml/automlbenchmark Precise benchmark version information could not be determined. constantpredictor version: stable
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,xgbclassifier=xgboost.sklearn.XGBClassifier) (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…
1 runs0 likes0 downloads0 reach0 impact
keras.engine.sequential.Sequential.63447f5f (1)
Automatically created keras flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors._classification.KNeighborsClassifier (6)
Classifier implementing the k-nearest neighbors vote.
1 runs0 likes1 downloads1 reach0 impact
sklearn.pipeline.Pipeline(OneHot=sklearn.preprocessing._encoders.OneHotEncoder,DTC=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…
1 runs0 likes0 downloads0 reach0 impact
keras.engine.sequential.Sequential.4a701663 (1)
Automatically created keras flow.
1 runs0 likes0 downloads0 reach0 impact
keras.engine.sequential.Sequential.3530aaa5 (1)
Automatically created keras flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (13)
A decision tree classifier.
1 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (14)
A decision tree classifier.
1 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (16)
A decision tree classifier.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,decisiontreeclassifier=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…
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and…
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier)) (1)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and…
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sklearn.linear_model._logistic.LogisticRegression (5)
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…
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sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder),variencethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,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 fit…
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (11)
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…
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sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder),scaling=sklearn.preprocessing._data.StandardScaler,variencethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,classifier=sklearn.svm._classes.SVC) (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…
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tensorflow.python.keras.engine.sequential.Sequential.59616071 (1)
Automatically created tensorflow flow.
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tensorflow.python.keras.engine.sequential.Sequential.59616071 (2)
Automatically created tensorflow flow.
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tensorflow.python.keras.engine.sequential.Sequential.5d790f77 (1)
Automatically created tensorflow flow.
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tensorflow.python.keras.engine.sequential.Sequential.2007cbb0 (1)
Automatically created tensorflow flow.
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tensorflow.python.keras.engine.sequential.Sequential.906af00b (1)
Automatically created tensorflow flow.
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tensorflow.python.keras.engine.sequential.Sequential.c8d201e7 (1)
Automatically created tensorflow flow.
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scikeras.wrappers.KerasClassifier (1)
Implementation of the scikit-learn classifier API for Keras. Below are a list of SciKeras specific parameters. For details on other parameters, please see the see the `tf.keras.Model documentation…
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (12)
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…
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weka.classifiers.trees.J48 (3)
Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
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weka.classifiers.trees.LMT (1)
Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees. Machine Learning. 95(1-2):161-205. Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree Induction. In: 9th European…
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weka.classifiers.trees.DecisionStump (1)
Weka implementation.
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weka.classifiers.functions.SGD (2)
Weka implementation.
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weka.classifiers.trees.RandomTree (1)
Weka implementation.
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sklearn.neighbors._classification.KNeighborsClassifier (8)
Classifier implementing the k-nearest neighbors vote.
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sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,clf=sklearn.neighbors._classification.KNeighborsClassifier) (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…
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sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.RobustScaler,clf=sklearn.neighbors._classification.KNeighborsClassifier) (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…
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sklearn.neural_network.multilayer_perceptron.MLPClassifier (18)
Multi-layer Perceptron classifier. This model optimizes the log-loss function using LBFGS or stochastic gradient descent. .. versionadded:: 0.18
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sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.ExtraTreeClassifier) (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 dataset…
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