DEVELOPMENT... OpenML
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flow
sklearn.ensemble.forest.RandomForestClassifier (9)
Flow generated by openml_run
12 runs0 likes5 downloads5 reach8 impact
classif.boosting (7)
Learner classif.boosting from package(s) adabag, rpart.
12 runs0 likes1 downloads1 reach0 impact
classif.hdrda (5)
Learner classif.hdrda from package(s) sparsediscrim.
12 runs0 likes1 downloads1 reach0 impact
classif.nodeHarvest (8)
Learner classif.nodeHarvest from package(s) nodeHarvest.
12 runs0 likes1 downloads1 reach0 impact
weka.RandomForest (10)
Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
12 runs0 likes0 downloads0 reach12 impact
weka.CostSensitiveClassifier_Bagging_RandomForest (2)
Weka implementation of CostSensitiveClassifier
12 runs0 likes0 downloads0 reach10 impact
weka.Bagging_LogitBoost_DecisionStump (3)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
12 runs0 likes0 downloads0 reach11 impact
weka.FilteredClassifier_NaiveBayes (2)
Weka implementation of FilteredClassifier
12 runs0 likes0 downloads0 reach0 impact
weka.BayesNet (2)
Weka implementation of BayesNet
12 runs0 likes0 downloads0 reach11 impact
weka.AttributeSelectedClassifier (1)
Weka implementation of AttributeSelectedClassifier
12 runs0 likes0 downloads0 reach12 impact
weka.FilteredClassifier_FilteredClassifier_IBk (1)
Weka implementation of FilteredClassifier
12 runs0 likes0 downloads0 reach12 impact
mlr.classif.qda (2)
Learner mlr.classif.qda from package(s) MASS.
12 runs0 likes0 downloads0 reach0 impact
mlr.classif.gbm.imputed.preproc (1)
Learner mlr.classif.gbm.imputed.preproc from package(s) gbm.
12 runs0 likes0 downloads0 reach0 impact
mlr.classif.boosting.imputed.preproc (1)
Learner mlr.classif.boosting.imputed.preproc from package(s) adabag, rpart.
12 runs0 likes0 downloads0 reach0 impact
weka.HoeffdingTree (7)
Geoff Hulten, Laurie Spencer, Pedro Domingos: Mining time-changing data streams. In: ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 97-106, 2001.
12 runs0 likes0 downloads0 reach0 impact
weka.JRip (9)
William W. Cohen: Fast Effective Rule Induction. In: Twelfth International Conference on Machine Learning, 115-123, 1995.
12 runs0 likes0 downloads0 reach0 impact
weka.AdaBoostM1 (3)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
12 runs0 likes0 downloads0 reach0 impact
weka.IBk (13)
D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Classifier=sklearn.ensemble.forest.RandomForestClassifier) (2)
Automatically created scikit-learn flow.
12 runs0 likes0 downloads0 reach11 impact
sklearn.pipeline.Pipeline(preprocessing=sklearn.preprocessing.data.RobustScaler,dimension_reduction=sklearn.decomposition.pca.PCA,classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,scaler=sklearn.preprocessing.data.StandardScaler,dimension_reduction=sklearn.decomposition.pca.PCA,Classifier=sklearn.ensemble.voting_classifier.VotingClassifier(knn=sklearn.neighbors.classification.KNeighborsClassifier,rf=sklearn.ensemble.forest.RandomForestClassifier)) (1)
Automatically created scikit-learn flow.
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(PCA=sklearn.decomposition.pca.PCA,Scaler=sklearn.preprocessing.data.StandardScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
12 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (41)
Automatically created scikit-learn flow.
12 runs0 likes0 downloads0 reach12 impact
sklearn.pipeline.Pipeline(model=sklearn.ensemble.forest.RandomForestClassifier) (2)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
12 runs0 likes0 downloads0 reach9 impact
sklearn.neighbors._regression.KNeighborsRegressor (1)
Regression based on k-nearest neighbors. The target is predicted by local interpolation of the targets associated of the nearest neighbors in the training set.
12 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model._ridge.Ridge (1)
Linear least squares with l2 regularization. Minimizes the objective function:: ||y - Xw||^2_2 + alpha * ||w||^2_2 This model solves a regression model where the loss function is the linear least…
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MinMaxScaler,step_1=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…
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.discriminant_analysis.LinearDiscriminantAnalysis),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…
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=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…
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier),step_1=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…
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.GenericUnivariateSelect,step_1=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…
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.Binarizer,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…
12 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.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…
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.cluster._agglomerative.FeatureAgglomeration,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…
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,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…
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.impute._knn.KNNImputer,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…
12 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.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…
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,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…
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.decomposition._fastica.FastICA,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…
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.RobustScaler,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…
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.RobustScaler,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…
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.RobustScaler,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…
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=randomsubgroups._randomsubgroups.RandomSubgroupClassifier) (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…
12 runs0 likes0 downloads0 reach0 impact
keras.engine.sequential.Sequential.3004bddd (5)
Automatically created keras flow.
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Median-Imputer=sklearn.preprocessing.imputation.Imputer,learner=sklearn.naive_bayes.GaussianNB) (2)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
12 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (3)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
12 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (25)
A decision tree classifier.
12 runs0 likes0 downloads0 reach12 impact
sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,decisiontreeregressor=sklearn.tree._classes.DecisionTreeRegressor) (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…
12 runs0 likes0 downloads0 reach0 impact
weka.RotationForest_PrincipalComponents_J48 (1)
Juan J. Rodriguez, Ludmila I. Kuncheva, Carlos J. Alonso (2006). Rotation Forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(10):1619-1630.…
11 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_DecisionStump (2)
Weka implementation of AttributeSelectedClassifier
11 runs0 likes1 downloads1 reach0 impact
classif.randomForest (38)
Learner classif.randomForest from package(s) randomForest.
11 runs0 likes1 downloads1 reach11 impact
classif.gbm (1)
Learner classif.gbm from package(s) gbm.
11 runs0 likes2 downloads2 reach0 impact
classif.boosting (3)
Learner classif.boosting from package(s) adabag, rpart.
11 runs0 likes1 downloads1 reach0 impact
classif.ksvm (3)
Learner classif.ksvm from package(s) kernlab.
11 runs0 likes1 downloads1 reach0 impact
classif.lda (8)
Learner classif.lda from package(s) MASS.
11 runs0 likes1 downloads1 reach0 impact
classif.multinom (4)
Learner classif.multinom from package(s) nnet.
11 runs0 likes1 downloads1 reach0 impact
classif.naiveBayes (4)
Learner classif.naiveBayes from package(s) e1071.
11 runs0 likes1 downloads1 reach0 impact
classif.nnet (2)
Learner classif.nnet from package(s) nnet.
11 runs0 likes1 downloads1 reach0 impact
classif.qda (1)
Learner classif.qda from package(s) MASS.
11 runs0 likes1 downloads1 reach0 impact
classif.randomForestSRC (2)
Learner classif.randomForestSRC from package(s) randomForestSRC.
11 runs0 likes1 downloads1 reach0 impact
classif.ranger (2)
Learner classif.ranger from package(s) ranger.
11 runs0 likes1 downloads1 reach0 impact
classif.rda (3)
Learner classif.rda from package(s) klaR.
11 runs0 likes1 downloads1 reach0 impact
classif.svm (4)
Learner classif.svm from package(s) e1071.
11 runs0 likes1 downloads1 reach0 impact
classif.xgboost (3)
Learner classif.xgboost from package(s) xgboost.
11 runs0 likes1 downloads1 reach0 impact
weka.CostSensitiveClassifier_ZeroR (2)
Weka implementation of CostSensitiveClassifier
11 runs0 likes2 downloads2 reach10 impact
classif.cforest (8)
Learner classif.cforest from package(s) party.
11 runs0 likes1 downloads1 reach0 impact
classif.extraTrees (7)
Learner classif.extraTrees from package(s) extraTrees.
11 runs0 likes1 downloads1 reach0 impact
weka.CostSensitiveClassifier_REPTree (7)
Weka implementation of CostSensitiveClassifier
11 runs0 likes0 downloads0 reach0 impact
xgboost.sklearn.XGBClassifier (3)
Automatically created sub-component.
11 runs0 likes0 downloads0 reach1 impact
weka.CostSensitiveClassifier_Logistic (3)
Weka implementation of CostSensitiveClassifier
11 runs0 likes0 downloads0 reach11 impact
weka.RandomizableFilteredClassifier_MultilayerPerceptron (1)
Weka implementation of RandomizableFilteredClassifier
11 runs0 likes0 downloads0 reach7 impact
weka.CostSensitiveClassifier_LMT (3)
Weka implementation of CostSensitiveClassifier
11 runs0 likes0 downloads0 reach0 impact
weka.MultilayerPerceptron (9)
Weka implementation of MultilayerPerceptron
11 runs0 likes0 downloads0 reach0 impact
mlr.classif.randomForest (30)
Learner mlr.classif.randomForest from package(s) randomForest.
11 runs0 likes0 downloads0 reach10 impact
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier (7)
Automatically created scikit-learn flow.
11 runs0 likes0 downloads0 reach2 impact
mlr.classif.ctree.imputed.preproc (1)
Learner mlr.classif.ctree.imputed.preproc from package(s) party.
11 runs0 likes0 downloads0 reach0 impact
mlr.classif.featureless.imputed.preproc (1)
Learner mlr.classif.featureless.imputed.preproc from package(s) mlr.
11 runs0 likes0 downloads0 reach0 impact
sklearn.dummy.DummyClassifier (5)
Automatically created scikit-learn flow.
11 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.StandardScaler,Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
11 runs0 likes0 downloads0 reach9 impact
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
11 runs0 likes0 downloads0 reach10 impact
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,model=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
11 runs0 likes0 downloads0 reach0 impact
mlr.classif.cforest (5)
Learner mlr.classif.cforest from package(s) party.
11 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.tree.tree.DecisionTreeClassifier) (4)
Automatically created scikit-learn flow.
11 runs0 likes0 downloads0 reach9 impact
sklearn.pipeline.Pipeline(1=sklearn.preprocessing.imputation.Imputer,2=sklearn.preprocessing.data.OneHotEncoder,3=sklearn.preprocessing.data.StandardScaler,4=sklearn.preprocessing._function_transformer.FunctionTransformer,5=sklearn.cluster.hierarchical.FeatureAgglomeration,6=sklearn.linear_model.stochastic_gradient.SGDClassifier) (1)
Automatically created scikit-learn flow.
11 runs0 likes0 downloads0 reach0 impact
keras.engine.training.Model.7736257d (1)
Automatically created keras flow.
11 runs0 likes0 downloads0 reach8 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPCA,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…
11 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._variance_threshold.VarianceThreshold,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…
11 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBinsDiscretizer,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…
11 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),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…
11 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MaxAbsScaler,step_1=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…
11 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._variance_threshold.VarianceThreshold,step_1=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…
11 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectKBest,step_1=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…
11 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.StandardScaler,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…
11 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.StandardScaler,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…
11 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.components.data_preprocessing.imputation.ImputationComponent,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…
11 runs0 likes0 downloads0 reach0 impact
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._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…
11 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,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…
11 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.decomposition._fastica.FastICA,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…
11 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (9)
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…
11 runs0 likes0 downloads0 reach9 impact
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,numeric=sklearn.preprocessing._data.StandardScaler),Classifier=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…
11 runs0 likes0 downloads0 reach0 impact

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