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weka.AttributeSelectedClassifier_SimpleLogistic (2)
Weka implementation of AttributeSelectedClassifier
3 runs0 likes0 downloads0 reach3 impact
weka.RandomizableFilteredClassifier_AdaBoostM1_SimpleLogistic (1)
Weka implementation of RandomizableFilteredClassifier
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) (2)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing.data.OneHotEncoder,extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
optimus.model_optimizer.ModelOptimizer(estimator=sklearn.linear_model.logistic.LogisticRegression) (2)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
weka.Bagging_LWL_DecisionStump (4)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
3 runs0 likes0 downloads0 reach0 impact
weka.MultiBoostAB_IBk (4)
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
3 runs0 likes0 downloads0 reach0 impact
weka.MultiBoostAB_JRip (4)
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
3 runs0 likes0 downloads0 reach0 impact
__main__.PythonChallengeClassifier (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach1 impact
sklearn.neighbors.classification.KNeighborsClassifier (9)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model.logistic.LogisticRegression (6)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach1 impact
mlr.classif.randomForest (33)
Learner mlr.classif.randomForest from package(s) randomForest.
3 runs0 likes0 downloads0 reach2 impact
weka.AttributeSelectedClassifier_SMO_Puk (1)
Weka implementation of AttributeSelectedClassifier
3 runs0 likes0 downloads0 reach0 impact
weka.LWL_Logistic (1)
Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003. C. Atkeson, A. Moore, S. Schaal (1996). Locally…
3 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_Vote_LMT (1)
Weka implementation of AttributeSelectedClassifier
3 runs0 likes0 downloads0 reach0 impact
rm.process.polynomial_by_binomial_classification_support_vector_machine (2)
A RapidMiner Workflow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors.classification.KNeighborsClassifier (11)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart (42)
Learner mlr.classif.rpart from package(s) rpart.
3 runs0 likes0 downloads0 reach2 impact
sklearn.ensemble.forest.ExtraTreesClassifier (7)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
mlr.classif.ranger (14)
Learner mlr.classif.ranger from package(s) ranger.
3 runs0 likes0 downloads0 reach2 impact
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.naive_bayes.GaussianNB) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
mlr.classif.glmnet (14)
Learner mlr.classif.glmnet from package(s) glmnet.
3 runs0 likes0 downloads0 reach0 impact
mlr.classif.glmnet (15)
Learner mlr.classif.glmnet from package(s) glmnet.
3 runs0 likes0 downloads0 reach1 impact
keras.wrappers.scikit_learn.KerasClassifier (1)
Automatically created sub-component.
3 runs0 likes0 downloads0 reach0 impact
weka.SMO (4)
J. Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning,…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,reg=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(MinMaxScaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(MinMaxScaler=sklearn.preprocessing.data.MinMaxScaler,Classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(StandardScaler=sklearn.preprocessing.data.StandardScaler,Classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(StandardScaler=sklearn.preprocessing.data.StandardScaler,Classifier=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(preprocessing=sklearn.preprocessing.data.StandardScaler,dimension_reduction=sklearn.decomposition.pca.PCA,classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(preprocessing=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach1 impact
sklearn.pipeline.Pipeline(StandardScaler=sklearn.preprocessing.data.MinMaxScaler,PCA=sklearn.decomposition.pca.PCA,LogisticRegression=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
mlr.classif.rpart (48)
Learner mlr.classif.rpart from package(s) rpart.
3 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.voting_classifier.VotingClassifier(lr=sklearn.linear_model.logistic.LogisticRegression,rf=sklearn.ensemble.forest.RandomForestClassifier,bnb=sklearn.naive_bayes.BernoulliNB) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(scaler2=sklearn.preprocessing.data.MinMaxScaler,estimator=sklearn.linear_model.stochastic_gradient.SGDClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(scaler2=sklearn.preprocessing.data.MinMaxScaler,PCA=sklearn.decomposition.pca.PCA,estimator=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Classifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(preprocessing=sklearn.preprocessing.data.StandardScaler,classifier=sklearn.ensemble.forest.ExtraTreesClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(preprocessing=sklearn.decomposition.pca.PCA,classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.voting_classifier.VotingClassifier(lr=sklearn.pipeline.Pipeline(Classifier2=sklearn.linear_model.logistic.LogisticRegression),knn=sklearn.pipeline.Pipeline(Scaler1=sklearn.preprocessing.data.StandardScaler,PCA1=sklearn.decomposition.pca.PCA,Classifier1=sklearn.neighbors.classification.KNeighborsClassifier),svc=sklearn.pipeline.Pipeline(Classifier3=sklearn.ensemble.forest.RandomForestClassifier)) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(preprocessing=sklearn.preprocessing.data.StandardScaler,pca=sklearn.decomposition.pca.PCA,classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(reduce_dim=sklearn.decomposition.pca.PCA,svm=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,rf=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach2 impact
imblearn.pipeline.Pipeline(SMOTE=imblearn.over_sampling.smote.SMOTE,Scaler=sklearn.preprocessing.data.StandardScaler,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
imblearn.pipeline.Pipeline(oversample=imblearn.over_sampling.adasyn.ADASYN,preprocessing=sklearn.preprocessing.data.StandardScaler,PCA=sklearn.decomposition.pca.PCA,classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.StandardScaler,PCA=sklearn.discriminant_analysis.LinearDiscriminantAnalysis,Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.discriminant_analysis.LinearDiscriminantAnalysis (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach3 impact
sklearn.pipeline.Pipeline(scalar=sklearn.preprocessing.data.StandardScaler,random_forest=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.MinMaxScaler,PCA=sklearn.decomposition.pca.PCA,svc=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
imblearn.pipeline.Pipeline(SMOTE=imblearn.over_sampling.smote.SMOTE,Scaler=sklearn.preprocessing.data.StandardScaler,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.MinMaxScaler,PCA=sklearn.decomposition.pca.PCA,Classifier=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(model=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Scaler=sklearn.preprocessing.data.MinMaxScaler,pca=sklearn.decomposition.pca.PCA,Classifier=sklearn.ensemble.bagging.BaggingClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(preprocessing=sklearn.preprocessing.data.StandardScaler,dimensionality_reduction=sklearn.decomposition.pca.PCA,classifier=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dimension_reduction=sklearn.decomposition.pca.PCA,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer,kerasclassifier=keras.wrappers.scikit_learn.KerasClassifier) (2)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach2 impact
mlr.classif.rpart (49)
Learner mlr.classif.rpart from package(s) rpart.
3 runs0 likes0 downloads0 reach0 impact
mlr.classif.ranger (18)
Learner mlr.classif.ranger from package(s) ranger.
3 runs0 likes0 downloads0 reach0 impact
weka.DecisionStump (10)
Weka implementation of DecisionStump
3 runs0 likes0 downloads0 reach1 impact
weka.Vote (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…
3 runs0 likes0 downloads0 reach0 impact
wrapper.AutoSklearnCV(estimator=autosklearn.estimators.AutoSklearnClassifier) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
arbok.autosklearn.AutoSklearnWrapper (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(stackingestimator=tpot.builtins.stacking_estimator.StackingEstimator(estimator=sklearn.ensemble.forest.ExtraTreesClassifier),logisticregression=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
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) (1)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
keras.wrappers.scikit_learn.KerasClassifier (7)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach0 impact
mlr.classif.ranger (19)
Learner mlr.classif.ranger from package(s) ranger.
3 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (38)
Automatically created scikit-learn flow.
3 runs0 likes0 downloads0 reach1 impact
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier) (5)
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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.svm._classes.SVC (12)
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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,decisiontreeregressor=sklearn.tree._classes.DecisionTreeRegressor) (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…
3 runs0 likes0 downloads0 reach0 impact
tensorflow.keras.engine.sequential.Sequential.dad96a35 (1)
Automatically created tensorflow flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors._classification.KNeighborsClassifier (19)
Classifier implementing the k-nearest neighbors vote.
3 runs0 likes0 downloads0 reach3 impact
tensorflow.keras.engine.sequential.Sequential.bc200b89 (1)
Automatically created tensorflow flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.naive_bayes.ComplementNB) (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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.naive_bayes.MultinomialNB) (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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (26)
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…
3 runs0 likes0 downloads0 reach2 impact
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),numeric=sklearn.pipeline.Pipeline(variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble._forest.RandomForestClassifier (30)
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…
3 runs0 likes0 downloads0 reach0 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) (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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors._classification.KNeighborsClassifier (21)
Classifier implementing the k-nearest neighbors vote.
3 runs0 likes0 downloads0 reach2 impact
sklearn.ensemble._forest.RandomForestClassifier (40)
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…
3 runs0 likes0 downloads0 reach1 impact
lightgbm.sklearn.LGBMClassifier (4)
LightGBM classifier.
3 runs0 likes0 downloads0 reach0 impact
tensorflow.keras.src.engine.sequential.Sequential.f426b76d (1)
Automatically created tensorflow flow.
3 runs0 likes0 downloads0 reach0 impact
tensorflow.keras.src.engine.sequential.Sequential.5b979f08 (1)
Automatically created tensorflow flow.
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=sklearn.feature_selection._univariate_selection.SelectKBest,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…
3 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.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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.Binarizer,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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._data.Binarizer,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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.PolynomialFeatures,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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.preprocessing._data.StandardScaler,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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.feature_selection._variance_threshold.VarianceThreshold,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…
3 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,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…
3 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.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…
3 runs0 likes0 downloads0 reach0 impact