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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (7)
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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (10)
A decision tree classifier.
1 runs0 likes0 downloads0 reach1 impact
keras.engine.sequential.Sequential.634a76ba (1)
Automatically created keras flow.
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amlb_constantpredictor (1)
This flow is generated by the automl benchmark: https://github.com/openml/automlbenchmark Precise benchmark version information could not be determined. constantpredictor version: stable
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (8)
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(standardscaler=sklearn.preprocessing.data.StandardScaler,xgbclassifier=xgboost.sklearn.XGBClassifier) (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…
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keras.engine.sequential.Sequential.63447f5f (1)
Automatically created keras flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.neighbors._classification.KNeighborsClassifier (6)
Classifier implementing the k-nearest neighbors vote.
1 runs0 likes1 downloads1 reach0 impact
sklearn.pipeline.Pipeline(OneHot=sklearn.preprocessing._encoders.OneHotEncoder,DTC=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…
1 runs0 likes0 downloads0 reach0 impact
keras.engine.sequential.Sequential.4a701663 (1)
Automatically created keras flow.
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keras.engine.sequential.Sequential.3530aaa5 (1)
Automatically created keras flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (13)
A decision tree classifier.
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sklearn.tree._classes.DecisionTreeClassifier (14)
A decision tree classifier.
1 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (16)
A decision tree classifier.
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sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,decisiontreeclassifier=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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble._forest.RandomForestClassifier) (1)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and…
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.pipeline.Pipeline(minmaxscaler=sklearn.preprocessing._data.MinMaxScaler,powertransformer=sklearn.preprocessing._data.PowerTransformer,robustscaler=sklearn.preprocessing._data.RobustScaler,standardscaler=sklearn.preprocessing._data.StandardScaler,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier)) (1)
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and…
1 runs0 likes1 downloads1 reach0 impact
sklearn.linear_model._logistic.LogisticRegression (5)
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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder),variencethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,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 fit…
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (11)
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(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.compose._column_transformer.ColumnTransformer(enc=sklearn.preprocessing._encoders.OneHotEncoder),scaling=sklearn.preprocessing._data.StandardScaler,variencethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,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 fit…
1 runs0 likes0 downloads0 reach0 impact
tensorflow.python.keras.engine.sequential.Sequential.59616071 (1)
Automatically created tensorflow flow.
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tensorflow.python.keras.engine.sequential.Sequential.59616071 (2)
Automatically created tensorflow flow.
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tensorflow.python.keras.engine.sequential.Sequential.5d790f77 (1)
Automatically created tensorflow flow.
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tensorflow.python.keras.engine.sequential.Sequential.2007cbb0 (1)
Automatically created tensorflow flow.
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tensorflow.python.keras.engine.sequential.Sequential.906af00b (1)
Automatically created tensorflow flow.
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tensorflow.python.keras.engine.sequential.Sequential.c8d201e7 (1)
Automatically created tensorflow flow.
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scikeras.wrappers.KerasClassifier (1)
Implementation of the scikit-learn classifier API for Keras. Below are a list of SciKeras specific parameters. For details on other parameters, please see the see the `tf.keras.Model documentation…
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (12)
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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weka.classifiers.trees.J48 (3)
Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
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weka.classifiers.trees.LMT (1)
Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees. Machine Learning. 95(1-2):161-205. Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree Induction. In: 9th European…
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weka.classifiers.trees.DecisionStump (1)
Weka implementation.
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weka.classifiers.functions.SGD (2)
Weka implementation.
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weka.classifiers.trees.RandomTree (1)
Weka implementation.
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sklearn.neighbors._classification.KNeighborsClassifier (8)
Classifier implementing the k-nearest neighbors vote.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,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 fit…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.RobustScaler,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 fit…
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sklearn.neural_network.multilayer_perceptron.MLPClassifier (18)
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.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.ExtraTreeClassifier) (2)
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…
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sklearn.pipeline.Pipeline(imputation=sklearn.preprocessing.imputation.Imputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=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…
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sklearn.pipeline.Pipeline(imputer=sklearn.impute.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 fit…
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sklearn.svm._classes.SVC (9)
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…
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sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer,std_scale=sklearn.preprocessing._data.StandardScaler,svr=sklearn.svm._classes.SVR) (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…
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sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer,std_scale=sklearn.preprocessing._data.StandardScaler,knn=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 fit…
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sklearn.pipeline.Pipeline(SimpleImputer=sklearn.impute._base.SimpleImputer,OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder,rf=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…
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sklearn.pipeline.Pipeline(SimpleImputer=sklearn.impute._base.SimpleImputer,OrdinalEncoder=sklearn.preprocessing._encoders.OrdinalEncoder,rf=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…
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sklearn.pipeline.Pipeline(SimpleImputer=sklearn.impute._base.SimpleImputer,OrdinalEncoder=sklearn.preprocessing._encoders.OrdinalEncoder,vc=sklearn.ensemble._voting.VotingClassifier(rf=sklearn.ensemble._forest.RandomForestClassifier,svc=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…
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sklearn.pipeline.Pipeline(ColumnTansformer=sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder),MaxAbsScaler=sklearn.preprocessing._data.MaxAbsScaler,svc=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…
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sklearn.pipeline.Pipeline(ColumnTansformer=sklearn.compose._column_transformer.ColumnTransformer(OneHotEncoder=sklearn.preprocessing._encoders.OneHotEncoder),hist=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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),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 fit…
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basic_generic_flow (1)
A simple generic flow used as an example. More details https://docs.openml.org/Python-examples/
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sklearn.tree._classes.DecisionTreeClassifier (23)
A decision tree classifier.
1 runs0 likes0 downloads0 reach1 impact
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (17)
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(encoder=sklearn.preprocessing._encoders.OrdinalEncoder,clf=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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mlr3.regr.catboost (1)
Learner regr.catboost from package(s) mlr3, mlr3extralearners, catboost.
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mlr.classif.rpart (63)
Learner mlr.classif.rpart from package(s) rpart.
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=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.svm._classes.NuSVC (1)
Nu-Support Vector Classification. Similar to SVC but uses a parameter to control the number of support vectors. The implementation is based on libsvm.
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mlr3.classif.rpart (3)
Learner classif.rpart from package(s) mlr3, rpart.
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sklearn.ensemble._forest.RandomForestClassifier (15)
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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mlr3.classif.rpart (4)
Learner classif.rpart from package(s) mlr3, rpart.
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mlr3.classif.rpart (6)
Learner classif.rpart from package(s) mlr3, rpart.
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mlr3.classif.rpart (9)
Learner classif.rpart from package(s) mlr3, rpart.
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mlr3.classif.rpart (10)
Learner classif.rpart from package(s) mlr3, rpart.
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mlr3.classif.rpart (11)
Learner classif.rpart from package(s) mlr3, rpart.
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mlr3.classif.rpart (12)
Learner classif.rpart from package(s) mlr3, rpart.
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sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier) (9)
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(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.tree._classes.DecisionTreeClassifier) (20)
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.tree._classes.DecisionTreeClassifier (28)
A decision tree classifier.
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sklearn.ensemble._forest.RandomForestClassifier (16)
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
sklearn.neighbors._classification.KNeighborsClassifier (12)
Classifier implementing the k-nearest neighbors vote.
1 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model._perceptron.Perceptron (1)
Linear perceptron classifier.
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sklearn.neighbors._classification.KNeighborsClassifier (13)
Classifier implementing the k-nearest neighbors vote.
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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…
1 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)),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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