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sklearn.pipeline.Pipeline(Classifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(knn=sklearn.neighbors.classification.KNeighborsClassifier,svc=sklearn.pipeline.Pipeline(preprocessing=sklearn.preprocessing.data.StandardScaler,dimension_reduction=sklearn.decomposition.pca.PCA,classifier=sklearn.svm.classes.SVC),rf=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(preprocessing=sklearn.preprocessing.data.StandardScaler,dimension_reduction=sklearn.decomposition.pca.PCA,classifier=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(other=sklearn.ensemble.forest.RandomForestClassifier,lr=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(knn=sklearn.neighbors.classification.KNeighborsClassifier,svc=sklearn.pipeline.Pipeline(preprocessing=sklearn.preprocessing.data.RobustScaler,dimension_reduction=sklearn.decomposition.pca.PCA,classifier=sklearn.svm.classes.SVC)) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,Classifier=sklearn.naive_bayes.BernoulliNB) (1)
Automatically created scikit-learn flow.
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sklearn.naive_bayes.BernoulliNB (4)
Automatically created scikit-learn flow.
2 runs0 likes0 downloads0 reach2 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)) (1)
Automatically created scikit-learn flow.
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mlr.classif.rpart.tuned (12)
Learner mlr.classif.rpart.tuned from package(s) rpart.
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sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,svm=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(rf=sklearn.ensemble.forest.RandomForestClassifier,knn=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,feat=sklearn.feature_selection.univariate_selection.SelectKBest,clf=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,scaler=sklearn.preprocessing.data.MinMaxScaler,PCA=sklearn.decomposition.pca.PCA,estimator=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.neighbors.classification.KNeighborsClassifier) (2)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(Classifier=sklearn.ensemble.voting_classifier.VotingClassifier(lr=sklearn.linear_model.logistic.LogisticRegression,knn=sklearn.neighbors.classification.KNeighborsClassifier,rf=sklearn.ensemble.forest.RandomForestClassifier)) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(Classifier=sklearn.naive_bayes.GaussianNB) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.RobustScaler,Classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(StandardScaler=sklearn.preprocessing.data.StandardScaler,Classifier=sklearn.ensemble.voting_classifier.VotingClassifier(lr=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.Binarizer,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.RobustScaler,Classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(lr=sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.Normalizer,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.linear_model.logistic.LogisticRegression),knn=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(lr=sklearn.pipeline.Pipeline(Classifier2=sklearn.linear_model.logistic.LogisticRegression),knn=sklearn.pipeline.Pipeline(Scaler1=sklearn.preprocessing.data.Normalizer,PCA1=sklearn.decomposition.pca.PCA,Classifier1=sklearn.neighbors.classification.KNeighborsClassifier),svc=sklearn.pipeline.Pipeline(Classifier3=sklearn.svm.classes.SVC)) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (3)
Automatically created scikit-learn flow.
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imblearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,logistic=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
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imblearn.pipeline.Pipeline(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,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (2)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.MinMaxScaler,knn=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(bg=sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.neighbors.classification.KNeighborsClassifier),rbf=sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC)) (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(bg=sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.neighbors.classification.KNeighborsClassifier),clf=sklearn.ensemble.bagging.BaggingClassifier(base_estimator=sklearn.svm.classes.SVC)) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,gaussiannb=sklearn.naive_bayes.GaussianNB) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(feature_reduce=sklearn.decomposition.pca.PCA,classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(preprocessing=sklearn.preprocessing.data.MinMaxScaler,dimensionality_reduction=sklearn.decomposition.pca.PCA,classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(preprocessing=sklearn.preprocessing.data.MinMaxScaler,PCA=sklearn.decomposition.pca.PCA,classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,rf=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(reduce_dim=sklearn.decomposition.pca.PCA,clf=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.StandardScaler,logistic=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(colorFix=sklearn.preprocessing._function_transformer.FunctionTransformer,scaler=sklearn.preprocessing.data.MinMaxScaler,rf=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(scaling=sklearn.preprocessing.data.StandardScaler,PCA=sklearn.decomposition.pca.PCA,tree=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(svm=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,svm=sklearn.svm.classes.SVC) (2)
Automatically created scikit-learn flow.
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(scalar=sklearn.preprocessing.data.StandardScaler,nearest_neighbor=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC) (3)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.StandardScaler,PCA=sklearn.decomposition.pca.PCA,knn=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,rfc=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(preprocessing=sklearn.preprocessing.data.RobustScaler,pca=sklearn.decomposition.pca.PCA,classifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,dimreduct=sklearn.decomposition.pca.PCA,gb=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,estimator=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.MinMaxScaler,logistic=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
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imblearn.pipeline.Pipeline(ADASYN=imblearn.over_sampling.adasyn.ADASYN,Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.StandardScaler,Classifier=sklearn.naive_bayes.GaussianNB) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(rescale=sklearn.preprocessing.data.StandardScaler,clf=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
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imblearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,pac=sklearn.decomposition.pca.PCA,oversample=imblearn.over_sampling.adasyn.ADASYN,classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(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.
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imblearn.pipeline.Pipeline(ada=imblearn.over_sampling.adasyn.ADASYN,scaler=sklearn.preprocessing.data.StandardScaler,knn=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer,kerasclassifier=keras.wrappers.scikit_learn.KerasClassifier) (1)
Automatically created scikit-learn flow.
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keras.wrappers.scikit_learn.KerasClassifier (5)
Automatically created scikit-learn flow.
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sklearn.ensemble.forest.RandomForestRegressor (1)
Automatically created scikit-learn flow.
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weka.RandomSubSpace (1)
Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL…
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weka.AutoWEKAClassifier (3)
Weka implementation of AutoWEKAClassifier
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weka.IterativeClassifierOptimizer (1)
Weka implementation of IterativeClassifierOptimizer
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weka.RandomTree (17)
Weka implementation of RandomTree
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rm.process(bagging(normalize,principal_component_analysis,support_vector_machine,group_models)) (1)
A RapidMiner Flow
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wrappers.tpot.TpotWrapper (2)
Automatically created scikit-learn flow.
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arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder)) (1)
Automatically created scikit-learn flow.
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arbok.autosklearn.AutoSklearnWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder)) (3)
Automatically created scikit-learn flow.
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arbok.tpot.TPOTWrapper(preprocessor=sklearn.pipeline.Pipeline(conditionalimputer=arbok.preprocessing.ConditionalImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder)) (5)
Automatically created scikit-learn flow.
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keras.wrappers.scikit_learn.KerasClassifier (6)
Automatically created scikit-learn flow.
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arbok.tpot.TPOTWrapper(preprocessor=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.pipeline.Pipeline(nystroem=sklearn.kernel_approximation.Nystroem,featureagglomeration=sklearn.cluster.hierarchical.FeatureAgglomeration,gaussiannb=sklearn.naive_bayes.GaussianNB) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier) (2)
Automatically created scikit-learn flow.
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sklearn.ensemble.forest.ExtraTreesClassifier (9)
Automatically created scikit-learn flow.
2 runs0 likes0 downloads0 reach2 impact
sklearn.pipeline.Pipeline(logisticregression=sklearn.linear_model.logistic.LogisticRegression) (3)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
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keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,MaxPooling2D,Activation,Conv2D,MaxPooling2D,Activation,Dropout,Conv2D,Flatten,Dense,Activation,Dense,Activation) (2)
Automatically created scikit-learn flow.
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keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,MaxPooling2D,Conv2D,MaxPooling2D,Flatten,Dense,Activation,Dense,Activation) (2)
Automatically created scikit-learn flow.
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keras.wrappers.scikit_learn.KerasClassifier(InputLayer,Reshape,Conv2D,MaxPooling2D,Conv2D,Conv2D,Conv2D,Merge,Activation,Conv2D,Conv2D,Conv2D,Merge,Activation,Conv2D,Conv2D,Conv2D,Merge,Activation,MaxPooling2D,Conv2D,Conv2D,Conv2D,Merge,Activation,Conv2D,Conv2D,Conv2D,Merge,Activation,Conv2D,Conv2D,Conv2D,Merge,Activation,Conv2D,Conv2D,Conv2D,Merge,Activation,MaxPooling2D,Conv2D,Conv2D,Conv2D,Merge,Activation,Dropout,Conv2D,AveragePooling2D,Flatten,Dense) (2)
Automatically created scikit-learn flow.
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keras.wrappers.scikit_learn.KerasClassifier(Reshape,Conv2D,MaxPooling2D,Activation,Conv2D,MaxPooling2D,Activation,Dropout,Conv2D,Flatten,Dense,Activation,Dense,Activation) (4)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier) (4)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
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sklearn.neighbors._classification.KNeighborsClassifier (15)
Classifier implementing the k-nearest neighbors vote.
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (22)
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…
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tensorflow.keras.engine.sequential.Sequential.ac472757 (1)
Automatically created tensorflow flow.
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sklearn.ensemble._forest.RandomForestClassifier (20)
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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sklearn.ensemble._forest.RandomForestClassifier (21)
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.randomForest (53)
Learner mlr.classif.randomForest from package(s) randomForest.
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mlr.classif.randomForest (54)
Learner mlr.classif.randomForest from package(s) randomForest.
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mlr.classif.rpart.preproc (21)
Learner mlr.classif.rpart.preproc from package(s) rpart.
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mlr.classif.ranger (24)
Learner mlr.classif.ranger from package(s) ranger.
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sklearn.ensemble._forest.RandomForestClassifier (22)
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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sklearn.ensemble._forest.RandomForestClassifier (23)
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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sklearn.neighbors._classification.KNeighborsClassifier (17)
Classifier implementing the k-nearest neighbors vote.
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tensorflow.keras.engine.sequential.Sequential.24f03ffd (1)
Automatically created tensorflow flow.
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sklearn.ensemble._forest.RandomForestClassifier (35)
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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sklearn.ensemble._forest.RandomForestClassifier (36)
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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tensorflow.keras.src.engine.sequential.Sequential.20e79174 (1)
Automatically created tensorflow flow.
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sklearn.ensemble._forest.RandomForestClassifier (42)
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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tensorflow.keras.src.engine.functional.Functional.f3109ce2 (1)
Automatically created tensorflow flow.
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