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sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.RobustScaler,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach3 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.MinMaxScaler,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach6 impact
sklearn.ensemble.voting_classifier.VotingClassifier(svc=sklearn.svm.classes.SVC,kn=sklearn.neighbors.classification.KNeighborsClassifier,gnb=sklearn.naive_bayes.GaussianNB,logreg=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,knn=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach1 impact
sklearn.pipeline.Pipeline(Scale=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,Classifier=sklearn.ensemble.voting_classifier.VotingClassifier(knn=sklearn.neighbors.classification.KNeighborsClassifier,svc=sklearn.svm.classes.SVC)) (1)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(PCA=sklearn.decomposition.pca.PCA,Scaler=sklearn.preprocessing.data.StandardScaler,clf=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.StandardScaler,Decomposition=sklearn.decomposition.pca.PCA,Classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.MinMaxScaler,pca=sklearn.decomposition.pca.PCA,Classifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach0 impact
weka.Stacking (2)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
7 runs0 likes0 downloads0 reach6 impact
weka.MultiClassClassifier (1)
Weka implementation of MultiClassClassifier
7 runs0 likes0 downloads0 reach7 impact
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (16)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach7 impact
sklearn.pipeline.Pipeline(gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (3)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach0 impact
keras.wrappers.scikit_learn.KerasClassifier(Reshape,Lambda,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Conv2D,Conv2D,Conv2D,MaxPooling2D,BatchNormalization,Flatten,Dense,Dropout,Dense,Dropout,Dense) (1)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (19)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach6 impact
sklearn.neighbors.classification.KNeighborsClassifier (32)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach7 impact
sklearn.neighbors.classification.KNeighborsClassifier (35)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach3 impact
sklearn.ensemble.forest.RandomForestClassifier (52)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach7 impact
mxnet.gluon.nn.basic_layers.HybridSequential.abab5292 (1)
Automatically created MXNet flow.
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
7 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.ExtraTreeClassifier (1)
An extremely randomized tree classifier. Extra-trees differ from classic decision trees in the way they are built. When looking for the best split to separate the samples of a node into two groups,…
7 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…
7 runs0 likes0 downloads0 reach0 impact
keras.engine.sequential.Sequential.3004bddd (2)
Automatically created keras flow.
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._variance_threshold.VarianceThreshold,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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.discriminant_analysis.LinearDiscriminantAnalysis),step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier),step_1=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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier),step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier),step_1=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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MaxAbsScaler,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
7 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.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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._pca.PCA,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…
7 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.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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.PolynomialFeatures,step_2=sklearn.tree._classes.DecisionTreeClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.cluster._agglomerative.FeatureAgglomeration,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…
7 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.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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.MinMaxScaler,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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._discretization.KBinsDiscretizer,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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.decomposition._pca.PCA,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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.PolynomialFeatures,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…
7 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._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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.cluster._agglomerative.FeatureAgglomeration,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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.components.data_preprocessing.imputation.ImputationComponent,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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._forest.RandomForestClassifier),step_2=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),step_2=sklearn.ensemble._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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.util.sklearn.StackingEstimator(estimator=sklearn.naive_bayes.BernoulliNB),step_2=sklearn.ensemble._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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.decomposition._kernel_pca.KernelPCA,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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._discretization.KBinsDiscretizer,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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.impute._base.MissingIndicator,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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.Normalizer,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…
7 runs0 likes0 downloads0 reach0 impact
keras.engine.sequential.Sequential.3004bddd (4)
Automatically created keras flow.
7 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors._regression.KNeighborsRegressor (2)
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.
7 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (20)
A decision tree classifier.
7 runs0 likes0 downloads0 reach7 impact
sklearn.linear_model.logistic.LogisticRegression (35)
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 cross-…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing.data.MinMaxScaler,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.tree.tree.DecisionTreeClassifier,ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier) (2)
Soft Voting/Majority Rule classifier for unfitted estimators. .. versionadded:: 0.17
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier,KNN=sklearn.neighbors.classification.KNeighborsClassifier,RFC=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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.tree.tree.DecisionTreeClassifier,ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier)) (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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(pca=sklearn.decomposition.pca.PCA,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier) (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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier) (15)
An AdaBoost classifier. An AdaBoost [1] classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset…
7 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble._forest.RandomForestClassifier (10)
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…
7 runs0 likes0 downloads0 reach6 impact
sklearn.svm._classes.NuSVC (2)
Nu-Support Vector Classification. Similar to SVC but uses a parameter to control the number of support vectors. The implementation is based on libsvm.
7 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(categorical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder),model=sklearn.linear_model._logistic.LogisticRegression) (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…
7 runs0 likes0 downloads0 reach0 impact
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,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier) (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…
7 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble._forest.RandomForestClassifier (28)
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…
7 runs0 likes0 downloads0 reach7 impact
weka.RotationForest_PrincipalComponents_ZeroR (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.…
6 runs0 likes1 downloads1 reach0 impact
weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_BFTree (2)
Weka implementation of AttributeSelectedClassifier
6 runs0 likes1 downloads1 reach0 impact
weka.Stacking_REPTree (1)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
6 runs0 likes3 downloads3 reach2 impact
classif.sda.tuned (5)
Learner classif.sda.tuned from package(s) sda.
6 runs0 likes1 downloads1 reach0 impact
classif.logreg (1)
Learner classif.logreg from package(s) stats.
6 runs0 likes2 downloads2 reach5 impact
classif.rpart (20)
Learner classif.rpart from package(s) rpart.
6 runs0 likes2 downloads2 reach1 impact
classif.lda (6)
Learner classif.lda from package(s) MASS.
6 runs0 likes1 downloads1 reach0 impact
classif.PART (1)
Learner classif.PART from package(s) RWeka.
6 runs0 likes1 downloads1 reach0 impact
classif.plr (2)
Learner classif.plr from package(s) stepPlr.
6 runs0 likes1 downloads1 reach0 impact
classif.bartMachine (4)
Learner classif.bartMachine from package(s) bartMachine.
6 runs0 likes1 downloads1 reach0 impact
sklearn.ensemble.bagging.BaggingClassifier (2)
Flow generated by run_task
6 runs0 likes1 downloads1 reach0 impact
mlr.classif.randomForest.filtered.oversampled (3)
Learner mlr.classif.randomForest.filtered.oversampled from package(s) mlr, randomForestSRC, randomForest.
6 runs0 likes1 downloads1 reach0 impact
classif.qda (6)
Learner classif.qda from package(s) MASS.
6 runs0 likes1 downloads1 reach0 impact
classif.lqa (7)
Learner classif.lqa from package(s) lqa.
6 runs0 likes1 downloads1 reach0 impact
weka.MultiSearch_J48 (2)
Weka implementation of MultiSearch
6 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart (15)
Learner mlr.classif.rpart from package(s) rpart.
6 runs0 likes0 downloads0 reach0 impact
mlr.classif.randomForest (13)
Learner mlr.classif.randomForest from package(s) randomForest.
6 runs0 likes0 downloads0 reach0 impact
mlr.classif.ksvm (2)
Learner mlr.classif.ksvm from package(s) kernlab.
6 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart.bagged (3)
Learner mlr.classif.rpart.bagged from package(s) rpart.
6 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart (19)
Learner mlr.classif.rpart from package(s) rpart.
6 runs0 likes0 downloads0 reach0 impact
mlr.classif.C50.bagged (1)
Learner mlr.classif.C50.bagged from package(s) C50.
6 runs0 likes0 downloads0 reach0 impact
mlr.classif.C50 (2)
Learner mlr.classif.C50 from package(s) C50.
6 runs0 likes0 downloads0 reach0 impact
mlr.classif.ctree (5)
Learner mlr.classif.ctree from package(s) party.
6 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart.bagged (5)
Learner mlr.classif.rpart.bagged from package(s) rpart.
6 runs0 likes0 downloads0 reach0 impact
mlr.classif.C50.bagged (7)
Learner mlr.classif.C50.bagged from package(s) C50.
6 runs0 likes0 downloads0 reach0 impact
mlr.classif.C50 (17)
Learner mlr.classif.C50 from package(s) C50.
6 runs0 likes0 downloads0 reach0 impact
mlr.classif.randomForest (17)
Learner mlr.classif.randomForest from package(s) randomForest.
6 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart.bagged (9)
Learner mlr.classif.rpart.bagged from package(s) rpart.
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mlr.classif.C50.bagged (8)
Learner mlr.classif.C50.bagged from package(s) C50.
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mlr.classif.rpart (21)
Learner mlr.classif.rpart from package(s) rpart.
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mlr.classif.C50 (18)
Learner mlr.classif.C50 from package(s) C50.
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mlr.classif.ctree (9)
Learner mlr.classif.ctree from package(s) party.
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weka.FilteredClassifier_CostSensitiveClassifier_RandomForest (1)
Weka implementation of FilteredClassifier
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weka.Vote_RandomForest (2)
Ludmila I. Kuncheva (2004). Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons, Inc.. J. Kittler, M. Hatef, Robert P.W. Duin, J. Matas (1998). On combining classifiers. IEEE…
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weka.CostSensitiveClassifier_Bagging_LogitBoost_DecisionStump (1)
Weka implementation of CostSensitiveClassifier
6 runs0 likes0 downloads0 reach4 impact