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sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
10 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_FilteredClassifier_J48 (1)
Weka implementation of CostSensitiveClassifier
10 runs0 likes0 downloads0 reach2 impact
weka.RandomizableFilteredClassifier_NaiveBayes (1)
Weka implementation of RandomizableFilteredClassifier
10 runs0 likes0 downloads0 reach9 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,mlpclassifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier) (1)
Automatically created scikit-learn flow.
10 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart (36)
Learner mlr.classif.rpart from package(s) rpart.
10 runs0 likes0 downloads0 reach0 impact
mlr.classif.xgboost (10)
Learner mlr.classif.xgboost from package(s) xgboost.
10 runs0 likes0 downloads0 reach0 impact
mlr.classif.randomForest (31)
Learner mlr.classif.randomForest from package(s) randomForest.
10 runs0 likes0 downloads0 reach1 impact
sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,classifier=sklearn.tree.tree.DecisionTreeClassifier) (2)
Automatically created scikit-learn flow.
10 runs0 likes0 downloads0 reach0 impact
mlr.classif.kknn.imputed.preproc (1)
Learner mlr.classif.kknn.imputed.preproc from package(s) !kknn.
10 runs0 likes0 downloads0 reach0 impact
mlr.classif.ksvm.imputed.preproc (1)
Learner mlr.classif.ksvm.imputed.preproc from package(s) kernlab.
10 runs0 likes0 downloads0 reach0 impact
mlr.classif.rda.imputed.preproc (1)
Learner mlr.classif.rda.imputed.preproc from package(s) klaR.
10 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart (45)
Learner mlr.classif.rpart from package(s) rpart.
10 runs0 likes0 downloads0 reach0 impact
sklearn.tree.tree.ExtraTreeClassifier (11)
Automatically created scikit-learn flow.
10 runs0 likes0 downloads0 reach9 impact
__main__.PythonChallengeClassifier (3)
Automatically created scikit-learn flow.
10 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.voting_classifier.VotingClassifier(xgb=xgboost.sklearn.XGBClassifier,svc=sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,svm=sklearn.svm.classes.SVC)) (1)
Automatically created scikit-learn flow.
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.Normalizer,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
10 runs0 likes0 downloads0 reach0 impact
sklearn.tree.tree.ExtraTreeClassifier (15)
Automatically created scikit-learn flow.
10 runs0 likes0 downloads0 reach10 impact
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,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,AveragePooling2D,Flatten,Dense) (1)
Automatically created scikit-learn flow.
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MinMaxScaler,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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._truncated_svd.TruncatedSVD,step_1=sklearn.naive_bayes.MultinomialNB) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._variance_threshold.VarianceThreshold,step_1=sklearn.naive_bayes.MultinomialNB) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectKBest,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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.GenericUnivariateSelect,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
10 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._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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.feature_selection._univariate_selection.SelectPercentile,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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.Normalizer,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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.RobustScaler,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…
10 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.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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.impute._knn.KNNImputer,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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.feature_selection._univariate_selection.SelectPercentile,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…
10 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._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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,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…
10 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.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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,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…
10 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.tree._classes.DecisionTreeClassifier),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…
10 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.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…
10 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.ensemble._weight_boosting.AdaBoostClassifier),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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.decomposition._kernel_pca.KernelPCA,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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.decomposition._factor_analysis.FactorAnalysis,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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.Normalizer,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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.decomposition._kernel_pca.KernelPCA,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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.impute._knn.KNNImputer,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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.impute._knn.KNNImputer,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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.impute._knn.KNNImputer,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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model._base.LinearRegression (2)
Ordinary least squares Linear Regression. LinearRegression fits a linear model with coefficients w = (w1, ..., wp) to minimize the residual sum of squares between the observed targets in the dataset,…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,mlpclassifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier) (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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,pca=sklearn.decomposition.pca.PCA,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (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…
10 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model.logistic.LogisticRegression (38)
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-…
10 runs0 likes0 downloads0 reach0 impact
sklearn.svm._classes.SVC (17)
C-Support Vector Classification. The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of…
10 runs0 likes0 downloads0 reach0 impact
classif.pamr (6)
Learner classif.pamr from package(s) pamr.
9 runs0 likes1 downloads1 reach0 impact
mlr.classif.ksvm (4)
Learner mlr.classif.ksvm from package(s) kernlab.
9 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_RandomForest (2)
Weka implementation of FilteredClassifier
9 runs0 likes0 downloads0 reach8 impact
weka.CostSensitiveClassifier_SimpleLogistic (1)
Weka implementation of CostSensitiveClassifier
9 runs0 likes0 downloads0 reach6 impact
weka.FilteredClassifier_RandomForest (3)
Weka implementation of FilteredClassifier
9 runs0 likes0 downloads0 reach8 impact
DeepForest.DeepForest (4)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach0 impact
__main__.AUC_Booster (1)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=helper.dual_imputer.DualImputer,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures,pca=sklearn.decomposition.pca.PCA,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach0 impact
weka.RandomizableFilteredClassifier_Bagging_RandomForest (1)
Weka implementation of RandomizableFilteredClassifier
9 runs0 likes0 downloads0 reach4 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=sklearn.naive_bayes.MultinomialNB) (1)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach0 impact
weka.BayesNet_GeneticSearch (1)
Weka implementation of BayesNet
9 runs0 likes1 downloads1 reach0 impact
weka.RotationForest_PrincipalComponents_LMT (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.…
9 runs0 likes1 downloads1 reach0 impact
weka.Bagging_BayesianLogisticRegression (2)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
9 runs0 likes1 downloads1 reach0 impact
classif.lda (5)
Learner classif.lda from package(s) MASS.
9 runs0 likes1 downloads1 reach0 impact
weka.Vote_Bagging (1)
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…
9 runs0 likes0 downloads0 reach9 impact
weka.AttributeSelectedClassifier_HoeffdingTree (2)
Weka implementation of AttributeSelectedClassifier
9 runs0 likes0 downloads0 reach9 impact
mlr.classif.glmnet (9)
Learner mlr.classif.glmnet from package(s) glmnet.
9 runs0 likes0 downloads0 reach0 impact
sklearn.tree.tree.ExtraTreeClassifier (3)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach4 impact
sklearn.ensemble.forest.RandomForestClassifier (25)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach9 impact
mlr.classif.LiblineaRL2LogReg.imputed.dummied.preproc (1)
Learner mlr.classif.LiblineaRL2LogReg.imputed.dummied.preproc from package(s) LiblineaR.
9 runs0 likes0 downloads0 reach0 impact
mlr.classif.multinom.imputed.preproc (1)
Learner mlr.classif.multinom.imputed.preproc from package(s) nnet.
9 runs0 likes0 downloads0 reach0 impact
mlr.classif.nnet.imputed.preproc (1)
Learner mlr.classif.nnet.imputed.preproc from package(s) nnet.
9 runs0 likes0 downloads0 reach0 impact
mlr.classif.randomForest.imputed.preproc (1)
Learner mlr.classif.randomForest.imputed.preproc from package(s) randomForest.
9 runs0 likes0 downloads0 reach0 impact
mlr.classif.saeDNN.imputed.dummied.preproc (1)
Learner mlr.classif.saeDNN.imputed.dummied.preproc from package(s) deepnet.
9 runs0 likes0 downloads0 reach0 impact
mlr.classif.mda.imputed.preproc (1)
Learner mlr.classif.mda.imputed.preproc from package(s) !mda.
9 runs0 likes0 downloads0 reach0 impact
mlr.classif.gausspr.imputed.preproc (1)
Learner mlr.classif.gausspr.imputed.preproc from package(s) kernlab.
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (13)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.RobustScaler,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach6 impact
sklearn.ensemble.voting_classifier.VotingClassifier(gb=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier,svc=sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,svm=sklearn.svm.classes.SVC)) (1)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(PCA=sklearn.decomposition.pca.PCA,Scaler=sklearn.preprocessing.data.StandardScaler,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.RobustScaler,Classifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach1 impact
sklearn.pipeline.Pipeline(Classifier=sklearn.ensemble.forest.RandomForestClassifier) (3)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach5 impact
sklearn.pipeline.Pipeline(Decomposition=sklearn.decomposition.pca.PCA,Classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach0 impact
weka.BayesNet (4)
Weka implementation of BayesNet
9 runs0 likes0 downloads0 reach8 impact
weka.Vote (3)
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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors.classification.KNeighborsClassifier (25)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach9 impact
keras.wrappers.scikit_learn.KerasClassifier(InputLayer,Reshape,Conv2D,MaxPooling2D,Conv2D,Conv2D,Conv2D,Concatenate,Activation,Conv2D,Conv2D,Conv2D,Concatenate,Activation,Conv2D,Conv2D,Conv2D,Concatenate,Activation,MaxPooling2D,Conv2D,Conv2D,Conv2D,Concatenate,Activation,Conv2D,Conv2D,Conv2D,Concatenate,Activation,Conv2D,Conv2D,Conv2D,Concatenate,Activation,Conv2D,Conv2D,Conv2D,Concatenate,Activation,MaxPooling2D,Conv2D,Conv2D,Conv2D,Concatenate,Activation,Dropout,Conv2D,AveragePooling2D,Flatten,Dense) (2)
Automatically created scikit-learn flow.
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,kneighborsregressor=sklearn.neighbors._regression.KNeighborsRegressor) (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…
9 runs0 likes0 downloads0 reach0 impact
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)),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…
9 runs0 likes0 downloads0 reach1 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier) (3)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
9 runs0 likes0 downloads0 reach8 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MinMaxScaler,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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MaxAbsScaler,step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectKBest,step_1=sklearn.ensemble._hist_gradient_boosting.gradient_boosting.HistGradientBoostingClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectKBest,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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._pca.PCA,step_1=sklearn.naive_bayes.BernoulliNB) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.MinMaxScaler,step_1=sklearn.naive_bayes.BernoulliNB) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier),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…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.naive_bayes.MultinomialNB) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit…
9 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectKBest,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…
9 runs0 likes0 downloads0 reach0 impact