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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier) (2)
Automatically created scikit-learn flow.
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mlr.regr.randomForest (3)
Learner mlr.regr.randomForest from package(s) randomForest.
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weka.NaiveBayesMultinomial (7)
Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.
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mlr.classif.randomForest (39)
Learner mlr.classif.randomForest from package(s) randomForest.
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weka.REPTree (20)
Weka implementation of REPTree
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mlr.classif.rpart (50)
Learner mlr.classif.rpart from package(s) rpart.
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mlr.classif.h2o.randomForest (2)
Learner mlr.classif.h2o.randomForest from package(s) h2o.
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rm.process(weka:W-J48) (1)
A RapidMiner Flow
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rm.process(normalize,concurrency:parallel_decision_tree,store) (1)
A RapidMiner Flow
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rm.process(normalize,weka:W-J48,store) (1)
A RapidMiner Flow
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rm.process(normalize,k_nn,store) (1)
A RapidMiner Flow
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weka.MultiClassClassifier (2)
Weka implementation of MultiClassClassifier
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sklearn.pipeline.Pipeline(Statistical_normalization=sklearn.preprocessing.data.StandardScaler,Decision_tree=sklearn.tree.tree.DecisionTreeClassifier) (1)
Automatically created scikit-learn flow.
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weka.SimpleLogistic (8)
Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees. Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree Induction. In: 9th European Conference on Principles and…
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rm.process(normalize,support_vector_machine,store) (2)
A RapidMiner Flow
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rm.process(normalize,concurrency:k_means,store) (1)
A RapidMiner Flow
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rm.process(concurrency:parallel_random_forest) (1)
A RapidMiner Flow
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rm.process(logistic_regression) (1)
A RapidMiner Flow
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rm.process(logistic_regression,optimize_selection) (1)
A RapidMiner Flow
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mlr.classif.randomForest (41)
Learner mlr.classif.randomForest from package(s) randomForest.
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weka.SGD (7)
Weka implementation of SGD
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mlr.classif.randomForest.smoted (1)
Learner mlr.classif.randomForest.smoted from package(s) randomForest.
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rm.process(normalize,concurrency:parallel_decision_tree,store) (2)
A RapidMiner Flow
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weka.ClassificationViaRegression (1)
E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
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rm.process(select_by_random,naive_bayes) (1)
A RapidMiner Flow
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weka.LOF (2)
Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jorg Sander (2000). LOF: Identifying Density-Based Local Outliers. ACM SIGMOD Record. 29(2):93-104.
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weka.IsolationForest (5)
Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou: Isolation Forest. In: ICDM, 413-422, 2008.
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weka.MLPClassifier (3)
Weka implementation of MLPClassifier
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weka.CostSensitiveClassifier (5)
Weka implementation of CostSensitiveClassifier
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mlr.classif.ada (3)
Learner mlr.classif.ada from package(s) ada, rpart.
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wrapper.AutoSklearnCV(estimator=autosklearn.estimators.AutoSklearnClassifier) (2)
Automatically created scikit-learn flow.
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autosklearnwrapper.wrapper.AutoSklearnWrapper(estimator=autosklearn.estimators.AutoSklearnClassifier) (1)
Automatically created scikit-learn flow.
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wrappers.tpot.TpotWrapper (1)
Automatically created scikit-learn flow.
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wrappers.autosklearn.AutoSklearnWrapper (1)
Automatically created scikit-learn flow.
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arbok.tpot.TPOTWrapper (1)
Automatically created scikit-learn flow.
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arbok.autosklearn.AutoSklearnWrapper (2)
Automatically created scikit-learn flow.
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arbok.autosklearn.AutoSklearnWrapper (3)
Automatically created scikit-learn flow.
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arbok.tpot.TPOTWrapper (2)
Automatically created scikit-learn flow.
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arbok.tpot.TPOTWrapper (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)) (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)) (2)
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)) (3)
Automatically created scikit-learn flow.
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sklearn.linear_model.logistic.LogisticRegression (13)
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)) (4)
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)) (5)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(featureagglomeration=sklearn.cluster.hierarchical.FeatureAgglomeration,kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(robustscaler=sklearn.preprocessing.data.RobustScaler,kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(rfe=sklearn.feature_selection.rfe.RFE(estimator=sklearn.ensemble.forest.ExtraTreesClassifier),linearsvc=sklearn.svm.classes.LinearSVC) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(featureagglomeration=sklearn.cluster.hierarchical.FeatureAgglomeration,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(binarizer=sklearn.preprocessing.data.Binarizer,stackingestimator=tpot.builtins.stacking_estimator.StackingEstimator(estimator=sklearn.ensemble.forest.RandomForestClassifier),gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(stackingestimator=tpot.builtins.stacking_estimator.StackingEstimator(estimator=sklearn.svm.classes.LinearSVC),pca=sklearn.decomposition.pca.PCA,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(robustscaler=sklearn.preprocessing.data.RobustScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (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) (1)
Automatically created scikit-learn flow.
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weka.IBk (15)
D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.
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openml.extras.extensions.KerasClassifierWrapper (1)
Automatically created scikit-learn flow.
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autonet.AutoNetClassification (1)
Automatically created scikit-learn flow.
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assignement6.SamuelsTurboClassifier3000.SamuelsTurboClassifier3000 (1)
Automatically created scikit-learn flow.
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keras.wrappers.scikit_learn.KerasClassifier(Reshape,ZeroPadding2D,Conv2D,ZeroPadding2D,Conv2D,MaxPooling2D,ZeroPadding2D,Conv2D,ZeroPadding2D,Conv2D,MaxPooling2D,ZeroPadding2D,Conv2D,ZeroPadding2D,Conv2D,ZeroPadding2D,Conv2D,ZeroPadding2D,Conv2D,MaxPooling2D,Flatten,Dropout,Dense,Dense) (1)
Automatically created scikit-learn flow.
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assignment6.SamuelsTurboClassifier3000.SamuelsTurboClassifier3000 (1)
Automatically created scikit-learn flow.
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weka.HoeffdingTree (8)
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.
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sklearn.pipeline.Pipeline(categorical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder),model=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…
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sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),kneighborsclassifier=sklearn.neighbors._classification.KNeighborsClassifier) (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…
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sklearn.linear_model._logistic.LogisticRegression (8)
Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the…
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sklearn.tree._classes.DecisionTreeClassifier (30)
A decision tree classifier.
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sklearn.neural_network._multilayer_perceptron.MLPClassifier (2)
Multi-layer Perceptron classifier. This model optimizes the log-loss function using LBFGS or stochastic gradient descent. .. versionadded:: 0.18
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sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,randomforestclassifier=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 fit…
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sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier) (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 fit…
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sklearn.ensemble._forest.RandomForestClassifier (19)
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.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,linearregression=sklearn.linear_model._base.LinearRegression) (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…
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sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestregressor=sklearn.ensemble._forest.RandomForestRegressor) (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…
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sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestregressor=sklearn.ensemble._forest.RandomForestRegressor) (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…
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sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestregressor=sklearn.ensemble._forest.RandomForestRegressor) (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…
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sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),decisiontreeregressor=sklearn.tree._classes.DecisionTreeRegressor) (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…
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torch.nn.modules.container.Sequential.d72a335796574729 (1)
Automatically created pytorch flow.
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torch.nn.modules.container.Sequential.2e1c4540e5937473 (1)
Automatically created pytorch flow.
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torch.nn.modules.container.Sequential.8502ce20bbbe1ad9 (1)
Automatically created pytorch flow.
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sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),reduce_dim=sklearn.feature_selection._from_model.SelectFromModel(estimator=sklearn.ensemble._forest.RandomForestClassifier),clf=sklearn.neighbors._classification.KNeighborsClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
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sklearn.ensemble._forest.RandomForestClassifier (24)
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.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (23)
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.ensemble._forest.RandomForestClassifier (26)
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.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing._data.StandardScaler),nominal=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),kneighborsregressor=sklearn.neighbors._regression.KNeighborsRegressor) (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…
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tensorflow.keras.engine.sequential.Sequential.367316f4 (1)
Automatically created tensorflow flow.
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tensorflow.keras.engine.sequential.Sequential.b7bbb36d (1)
Automatically created tensorflow flow.
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tensorflow.keras.engine.sequential.Sequential.8976669f (1)
Automatically created tensorflow flow.
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tensorflow.keras.engine.sequential.Sequential.cd00b152 (1)
Automatically created tensorflow flow.
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sklearn.pipeline.Pipeline(scale=sklearn.preprocessing._data.MinMaxScaler,estimator=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…
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (27)
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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amlb_RandomForest (1)
This flow is generated by the automl benchmark: https://github.com/openml/automlbenchmark.git Repository commit: d5c73433ffc6c57c88113a897213a6bc057e5846 RandomForest version: 1.2.2
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sklearn.pipeline.Pipeline(imputation=sklearn.impute._base.SimpleImputer,hotencoding=sklearn.preprocessing._encoders.OneHotEncoder,variencethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,clf=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…
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sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder),Classifier=sklearn.ensemble._forest.RandomForestClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
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sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing._data.StandardScaler,knnimputer=sklearn.impute._knn.KNNImputer)),Classifier=sklearn.svm._classes.SVC) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
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sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,standardscaler=sklearn.preprocessing._data.StandardScaler,knnimputer=sklearn.impute._knn.KNNImputer)),BayesinClassifier=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…
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sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler)),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…
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sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,simpleimputer=sklearn.impute._base.SimpleImputer),numeric=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler)),BayesinClassifier=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…
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sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),numeric=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer)),BayesinClassifier=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…
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (28)
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.ensemble._forest.RandomForestClassifier (31)
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.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestclassifier=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…
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sklearn.neighbors._classification.KNeighborsClassifier (22)
Classifier implementing the k-nearest neighbors vote.
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vonfry-test-flow (1)
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…
1 runs0 likes0 downloads0 reach0 impact