DEVELOPMENT... OpenML
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sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created sub-component.
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(voting=sklearn.ensemble.forest.RandomForestClassifier,weights=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,n_jobs=aucbooster.AUCBooster)) (1)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach0 impact
Voter.Voter (1)
Automatically created scikit-learn flow.
5 runs0 likes1 downloads1 reach0 impact
weka.CostSensitiveClassifier_ZeroR (3)
Weka implementation of CostSensitiveClassifier
5 runs0 likes0 downloads0 reach3 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,robustscaler=sklearn.preprocessing.data.RobustScaler,lr=__main__.LR) (1)
Automatically created scikit-learn flow.
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weka.FilteredClassifier_FilteredClassifier_FilteredClassifier_RandomForest (1)
Weka implementation of FilteredClassifier
5 runs0 likes0 downloads0 reach2 impact
weka.CostSensitiveClassifier_AdaBoostM1_RandomForest (1)
Weka implementation of CostSensitiveClassifier
5 runs0 likes0 downloads0 reach5 impact
sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
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weka.Bagging_AdaBoostM1_J48 (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.FilteredClassifier_RandomizableFilteredClassifier_Vote_SMO (1)
Weka implementation of FilteredClassifier
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weka.RandomizableFilteredClassifier_IBk (1)
Weka implementation of RandomizableFilteredClassifier
5 runs0 likes0 downloads0 reach3 impact
weka.CostSensitiveClassifier_SGD (1)
Weka implementation of CostSensitiveClassifier
5 runs0 likes0 downloads0 reach4 impact
weka.IBk (12)
D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.
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weka.FilteredClassifier_FilteredClassifier_FilteredClassifier (1)
Weka implementation of FilteredClassifier
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sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (2)
Automatically created scikit-learn flow.
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mlr.classif.rpart.tuned (8)
Learner mlr.classif.rpart.tuned from package(s) rpart.
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mlr.classif.ctree (14)
Learner mlr.classif.ctree from package(s) party.
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))) (2)
Automatically created scikit-learn flow.
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mlr.classif.naiveBayes.imputed.preproc (1)
Learner mlr.classif.naiveBayes.imputed.preproc from package(s) e1071.
5 runs0 likes0 downloads0 reach0 impact
mlr.classif.ranger.imputed.preproc (1)
Learner mlr.classif.ranger.imputed.preproc from package(s) ranger.
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sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (5)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach2 impact
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier (8)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach3 impact
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (7)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach1 impact
sklearn.ensemble.forest.RandomForestClassifier (29)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach5 impact
mlr.classif.naiveBayes (9)
Learner mlr.classif.naiveBayes from package(s) e1071.
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sklearn.tree.tree.DecisionTreeClassifier (18)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach4 impact
weka.ZeroR (16)
Weka implementation of ZeroR
5 runs0 likes0 downloads0 reach3 impact
sklearn.ensemble.voting_classifier.VotingClassifier(lr=sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,reg=sklearn.linear_model.logistic.LogisticRegression),knn=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.MinMaxScaler,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach3 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.MinMaxScaler,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach3 impact
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,svm=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach2 impact
sklearn.gaussian_process.gpc.GaussianProcessClassifier (2)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach5 impact
sklearn.ensemble.voting_classifier.VotingClassifier(knn=sklearn.neighbors.classification.KNeighborsClassifier,svc=sklearn.svm.classes.SVC,bnb=sklearn.naive_bayes.BernoulliNB) (1)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.voting_classifier.VotingClassifier(knn=sklearn.neighbors.classification.KNeighborsClassifier,svc=sklearn.pipeline.Pipeline(transformer=sklearn.preprocessing.data.StandardScaler,reduction=sklearn.decomposition.pca.PCA,estimator=sklearn.svm.classes.SVC)) (1)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.StandardScaler,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach3 impact
imblearn.pipeline.Pipeline(SMOTE=imblearn.over_sampling.smote.SMOTE,scaler=sklearn.preprocessing.data.StandardScaler,knn=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,model=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
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imblearn.pipeline.Pipeline(ADASYN=imblearn.over_sampling.adasyn.ADASYN,Scaler=sklearn.preprocessing.data.StandardScaler,Classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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imblearn.pipeline.Pipeline(ADASYN=imblearn.over_sampling.adasyn.ADASYN,Classifier=sklearn.tree.tree.DecisionTreeClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(reduce_dim=sklearn.decomposition.pca.PCA,logistic_reg=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
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weka.KStar (7)
John G. Cleary, Leonard E. Trigg: K*: An Instance-based Learner Using an Entropic Distance Measure. In: 12th International Conference on Machine Learning, 108-114, 1995.
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weka.MultiSearch (2)
Weka implementation of MultiSearch
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mlr.classif.rpart.tuned (13)
Learner mlr.classif.rpart.tuned from package(s) rpart.
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rm.process(normalize,principal_component_analysis,support_vector_machine,group_models) (1)
A RapidMiner Flow
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keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,MaxPooling2D,Conv2D,MaxPooling2D,Flatten,Dense,Activation,Dense,Activation) (1)
Automatically created scikit-learn flow.
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keras.wrappers.scikit_learn.KerasClassifier(InputLayer,Reshape,Reshape,ZeroPadding2D,Conv2D,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Activation,Conv2D,Conv2D,Add,BatchNormalization,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Activation,Conv2D,Conv2D,Add,BatchNormalization,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Activation,Conv2D,Conv2D,Add,BatchNormalization,Activation,AveragePooling2D,Flatten,Dense) (2)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach0 impact
xgboost.sklearn.XGBClassifier (7)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach3 impact
mlr.classif.rpart (53)
Learner mlr.classif.rpart from package(s) rpart.
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sklearn.tree.tree.DecisionTreeClassifier (36)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(features=sklearn.pipeline.Pipeline2(V1=sklearn.compose._column_transformer.ColumnTransformer1(identity=sklearn.preprocessing._function_transformer.FunctionTransformer0)),classifier=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
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mxnet.gluon.nn.basic_layers.HybridSequential.f4ce7786 (1)
Automatically created MXNet flow.
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mlr.regr.randomForest (4)
Learner mlr.regr.randomForest from package(s) randomForest.
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mxnet.gluon.nn.basic_layers.HybridSequential.cce30325 (1)
Automatically created MXNet flow.
5 runs0 likes0 downloads0 reach1 impact
mlr.costsens.classif.ranger (1)
Learner mlr.costsens.classif.ranger from package(s) ranger.
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sklearn.pipeline.Pipeline(svc=sklearn.svm.classes.SVC) (3)
Automatically created scikit-learn flow.
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sklearn.tree.tree.ExtraTreeClassifier (27)
Automatically created scikit-learn flow.
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Framework.custom_sklearn_pipeline.S1_Branch_Pipeline(cluster_block=Framework.building_blocks.ClusterBlock(test_set_model=sklearn.neighbors.classification.KNeighborsClassifier,training_set_model=sklearn.cluster.k_means_.KMeans),ensemble_block=Framework.building_blocks.EnsembleBlock,explainer_block=Framework.building_blocks.ExplainerBlock,processing_block=Framework.building_blocks.ProcessingBlock) (1)
Data -> Explainer_model -> Prediction
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xgboost.sklearn.XGBClassifier (10)
Implementation of the scikit-learn API for XGBoost classification.
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sklearn.naive_bayes.GaussianNB (18)
Gaussian Naive Bayes (GaussianNB) Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS…
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sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._variance_threshold.VarianceThreshold,step_1=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…
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sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.GenericUnivariateSelect,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…
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sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectPercentile,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…
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sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),step_1=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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._fastica.FastICA,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.StandardScaler,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._kernel_pca.KernelPCA,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.feature_selection._univariate_selection.SelectPercentile,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._kernel_pca.KernelPCA,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._kernel_pca.KernelPCA,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.MaxAbsScaler,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.data_preprocessing.imputation.ImputationComponent,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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._kernel_pca.KernelPCA,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.Normalizer,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.components.data_preprocessing.imputation.ImputationComponent,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.impute._knn.KNNImputer,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.cluster._agglomerative.FeatureAgglomeration,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=sklearn.feature_selection._univariate_selection.SelectPercentile,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.PolynomialFeatures,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.QuantileTransformer,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.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._discretization.KBinsDiscretizer,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.feature_selection._variance_threshold.VarianceThreshold,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.Normalizer,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.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.RobustScaler,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.feature_selection._variance_threshold.VarianceThreshold,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.StandardScaler,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._kernel_pca.KernelPCA,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…
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sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,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=sklearn.feature_selection._variance_threshold.VarianceThreshold,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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.feature_selection._variance_threshold.VarianceThreshold,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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._kernel_pca.KernelPCA,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…
5 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.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…
5 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.svm._classes.SVC),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…
5 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.naive_bayes.BernoulliNB),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…
5 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.Binarizer,step_1=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…
5 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.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…
5 runs0 likes0 downloads0 reach0 impact