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mlr.classif.ada (2)
Learner mlr.classif.ada from package(s) ada, rpart.
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sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier) (5)
Automatically created scikit-learn flow.
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openmlstudy14.preprocessing.ConditionalImputer (7)
Automatically created scikit-learn flow.
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sklearn.preprocessing.data.OneHotEncoder (20)
Automatically created scikit-learn flow.
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sklearn.feature_selection.variance_threshold.VarianceThreshold (13)
Automatically created scikit-learn flow.
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sklearn.ensemble.forest.RandomForestClassifier (36)
Automatically created scikit-learn flow.
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sklearn.neural_network.multilayer_perceptron.MLPClassifier (12)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(conditionalimputer=openmlstudy14.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,mlpclassifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.preprocessing.data.OneHotEncoder (21)
Automatically created scikit-learn flow.
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weka.GaussianProcesses (1)
David J.C. Mackay (1998). Introduction to Gaussian Processes. Dept. of Physics, Cambridge University, UK.
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sklearn.tree.tree.ExtraTreeClassifier (16)
Automatically created scikit-learn flow.
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sklearn.preprocessing.imputation.Imputer (21)
Automatically created scikit-learn flow.
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sklearn.preprocessing.data.OneHotEncoder (22)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (2)
Automatically created scikit-learn flow.
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sklearn.preprocessing.imputation.Imputer (22)
Automatically created scikit-learn flow.
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sklearn.decomposition.pca.PCA (5)
Automatically created scikit-learn flow.
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wrappers.autosklearn.AutoSklearnWrapper(estimator=autosklearn.estimators.AutoSklearnClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder) (1)
Automatically created scikit-learn flow.
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arbok.preprocessing.ConditionalImputer (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder) (2)
Automatically created scikit-learn flow.
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arbok.preprocessing.ConditionalImputer (2)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder) (3)
Automatically created scikit-learn flow.
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arbok.preprocessing.ConditionalImputer (3)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder) (4)
Automatically created scikit-learn flow.
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arbok.preprocessing.ConditionalImputer (4)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder) (5)
Automatically created scikit-learn flow.
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arbok.preprocessing.ConditionalImputer (5)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder) (6)
Automatically created scikit-learn flow.
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arbok.preprocessing.ConditionalImputer (6)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder) (7)
Automatically created scikit-learn flow.
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arbok.preprocessing.ConditionalImputer (7)
Automatically created scikit-learn flow.
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weka.kf.DecisionTree (1)
A Weka KnowledgeFlow using DecisionTree.kf
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weka.kf.MultilayerPerceptron (1)
A Weka KnowledgeFlow using MultilayerPerceptron.kf
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weka.kf.NaiveBayes (2)
A Weka KnowledgeFlow using NaiveBayes.kf
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sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold) (1)
Automatically created scikit-learn flow.
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sklearn.feature_selection.variance_threshold.VarianceThreshold (14)
Automatically created scikit-learn flow.
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tpot.builtins.one_hot_encoder.OneHotEncoder (1)
Automatically created scikit-learn flow.
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sklearn.kernel_approximation.Nystroem (2)
Automatically created scikit-learn flow.
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sklearn.cluster.hierarchical.FeatureAgglomeration (1)
Automatically created scikit-learn flow.
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tpot.builtins.stacking_estimator.StackingEstimator(estimator=sklearn.ensemble.forest.ExtraTreesClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.preprocessing.data.RobustScaler (4)
Automatically created scikit-learn flow.
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sklearn.feature_selection.rfe.RFE(estimator=sklearn.ensemble.forest.ExtraTreesClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.svm.classes.LinearSVC (6)
Automatically created scikit-learn flow.
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sklearn.preprocessing.data.Binarizer (2)
Automatically created scikit-learn flow.
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tpot.builtins.stacking_estimator.StackingEstimator(estimator=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.decomposition.pca.PCA (6)
Automatically created scikit-learn flow.
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tpot.builtins.stacking_estimator.StackingEstimator(estimator=sklearn.svm.classes.LinearSVC) (1)
Automatically created scikit-learn flow.
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.linear_model.stochastic_gradient.SGDClassifier)) (2)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.linear_model.stochastic_gradient.SGDClassifier) (2)
Automatically created scikit-learn flow.
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mylib.preprocessing_openml14.ConditionalImputer (2)
Automatically created scikit-learn flow.
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sklearn.preprocessing.data.OneHotEncoder (23)
Automatically created scikit-learn flow.
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sklearn.feature_selection.variance_threshold.VarianceThreshold (15)
Automatically created scikit-learn flow.
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sklearn.preprocessing.data.StandardScaler (8)
Automatically created scikit-learn flow.
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sklearn.linear_model.stochastic_gradient.SGDClassifier (6)
Automatically created scikit-learn flow.
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier)) (2)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier) (2)
Automatically created scikit-learn flow.
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sklearn.neural_network.multilayer_perceptron.MLPClassifier (13)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold) (2)
Automatically created scikit-learn flow.
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arbok.preprocessing.ConditionalImputer (8)
Automatically created scikit-learn flow.
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.LinearSVC)) (2)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.LinearSVC) (2)
Automatically created scikit-learn flow.
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sklearn.svm.classes.LinearSVC (7)
Automatically created scikit-learn flow.
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.SVC)) (2)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.svm.classes.SVC) (2)
Automatically created scikit-learn flow.
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sklearn.svm.classes.SVC (19)
Automatically created scikit-learn flow.
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.tree.tree.DecisionTreeClassifier)) (2)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(imputation=mylib.preprocessing_openml14.ConditionalImputer,one-hot-encoder=sklearn.preprocessing.data.OneHotEncoder,variance-thresholding=sklearn.feature_selection.variance_threshold.VarianceThreshold,feature-scaling=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.tree.tree.DecisionTreeClassifier) (2)
Automatically created scikit-learn flow.
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sklearn.tree.tree.DecisionTreeClassifier (23)
Automatically created scikit-learn flow.
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arbok.preprocessing.ConditionalImputer (9)
Automatically created scikit-learn flow.
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keras.wrappers.scikit_learn.KerasClassifierWrapper (1)
Automatically created scikit-learn flow.
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sklearn.preprocessing.data.OneHotEncoder (24)
Automatically created scikit-learn flow.
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sklearn.feature_selection.variance_threshold.VarianceThreshold (16)
Automatically created scikit-learn flow.
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sklearn.svm.classes.SVC (20)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,onehot=sklearn.preprocessing.data.OneHotEncoder,classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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sklearn.preprocessing.imputation.Imputer (24)
Automatically created scikit-learn flow.
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weka.VotedPerceptron (7)
Y. Freund, R. E. Schapire: Large margin classification using the perceptron algorithm. In: 11th Annual Conference on Computational Learning Theory, New York, NY, 209-217, 1998.
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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,Add,BatchNormalization,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Activation,Conv2D,Conv2D,Add,BatchNormalization,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Activation,Conv2D,Add,BatchNormalization,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Activation,Conv2D,Conv2D,Add,BatchNormalization,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Activation,Conv2D,Add,BatchNormalization,Activation,AveragePooling2D,Flatten,Dense) (1)
Automatically created scikit-learn flow.
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keras.wrappers.scikit_learn.KerasClassifier(InputLayer,Reshape,ZeroPadding2D,Conv2D,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,Conv2D,BatchNormalization,Activation,Conv2D,BatchNormalization,Add,Activation,AveragePooling2D,Flatten,Dense) (1)
Automatically created scikit-learn flow.
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sklearn.neural_network.multilayer_perceptron.MLPClassifier (14)
Automatically created scikit-learn flow.
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mlr.classif.randomForest (42)
Learner mlr.classif.randomForest from package(s) randomForest.
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sklearn.preprocessing.imputation.Imputer (25)
Automatically created scikit-learn flow.
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sklearn.preprocessing.data.OneHotEncoder (25)
Automatically created scikit-learn flow.
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sklearn.ensemble.forest.RandomForestClassifier (39)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,kerasclassifier=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(Reshape,Conv2D,MaxPooling2D,Conv2D,MaxPooling2D,Flatten,Dense,Activation,Dense,Activation) (4)
Automatically created scikit-learn flow.
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__main__.RandomForestWithPCA (1)
Automatically created scikit-learn flow.
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__main__.GradientBoostingWithPCA (1)
Automatically created scikit-learn flow.
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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)) (3)
Automatically created scikit-learn flow.
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openmlstudy14.preprocessing.ConditionalImputer (8)
Automatically created scikit-learn flow.
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sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier) (5)
Automatically created scikit-learn flow.
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sklearn.tree.tree.DecisionTreeClassifier (24)
Automatically created scikit-learn flow.
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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))) (3)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier) (6)
Automatically created scikit-learn flow.
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sklearn.ensemble.forest.RandomForestClassifier (40)
Automatically created scikit-learn flow.
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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.forest.RandomForestClassifier)) (4)
Automatically created scikit-learn flow.
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__main__.NNAugmentedInput (1)
Automatically created scikit-learn flow.
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openmlstudy99.preprocessing.ConditionalImputer (1)
Automatically created scikit-learn flow.
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openmldefaults.search.default_search.DefaultSearchCV(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))) (1)
Automatically created scikit-learn flow.
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sklearn.preprocessing.imputation.Imputer (26)
Automatically created scikit-learn flow.
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