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101 results
sklearn.svm.classes.SVC (1)
Flow generated by run_task
3 runs0 likes1 downloads1 reach0 impact
sklearn.ensemble.weight_boosting.AdaBoostClassifier (1)
Flow generated by run_task
21 runs0 likes1 downloads1 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (41)
Automatically created scikit-learn flow.
12 runs0 likes0 downloads0 reach12 impact
sklearn.pipeline.Pipeline(imputation=preprocessing.ConditionalImputer2,hotencoding=sklearn.preprocessing._encoders.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
preprocessing.ConditionalImputer2 (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.feature_selection.variance_threshold.VarianceThreshold (20)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputation=preprocessing.ConditionalImputer2,catencoding=preprocessing.MultiLabelEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
3475 runs0 likes0 downloads0 reach0 impact
preprocessing.MultiLabelEncoder (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(encoding=category_encoders.target_encoder.TargetEncoder,algorithm=sklearn.naive_bayes.GaussianNB) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
category_encoders.target_encoder.TargetEncoder (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.naive_bayes.GaussianNB (9)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer.ColumnTransformer(imputer=sklearn.impute.SimpleImputer,cat_encoder=category_encoders.target_encoder.TargetEncoder),algorithm=sklearn.naive_bayes.GaussianNB) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(imputer=sklearn.impute.SimpleImputer,cat_encoder=category_encoders.target_encoder.TargetEncoder) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
category_encoders.target_encoder.TargetEncoder (2)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.naive_bayes.GaussianNB (10)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer.ColumnTransformer(imputer=sklearn.impute.SimpleImputer,cat_encoder=category_encoders.target_encoder.TargetEncoder),algorithm=sklearn.naive_bayes.GaussianNB) (2)
Automatically created scikit-learn flow.
2 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(imputer=sklearn.impute.SimpleImputer,cat_encoder=category_encoders.target_encoder.TargetEncoder) (2)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.impute.SimpleImputer (8)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
category_encoders.target_encoder.TargetEncoder (3)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.naive_bayes.GaussianNB (12)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer.ColumnTransformer(imputer=sklearn.impute.SimpleImputer,cat_encoder=category_encoders.target_encoder.TargetEncoder),algorithm=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (49)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach1 impact
sklearn.tree.tree.DecisionTreeClassifier (36)
Automatically created scikit-learn flow.
5 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.ExtraTreesClassifier (11)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.naive_bayes.MultinomialNB (5)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer.ColumnTransformer(imputer=sklearn.impute.SimpleImputer,cat_encoder=category_encoders.target_encoder.TargetEncoder),algorithm=sklearn.ensemble.forest.RandomForestClassifier) (2)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(imputer=sklearn.impute.SimpleImputer,cat_encoder=category_encoders.target_encoder.TargetEncoder) (3)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.impute.SimpleImputer (9)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (50)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing._encoders.OrdinalEncoder (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer.ColumnTransformer(imputer=sklearn.impute.SimpleImputer,basic_encoder=category_encoders.one_hot.OneHotEncoder),algorithm=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(imputer=sklearn.impute.SimpleImputer,basic_encoder=category_encoders.one_hot.OneHotEncoder) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.impute.SimpleImputer (10)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
category_encoders.one_hot.OneHotEncoder (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (51)
Automatically created scikit-learn flow.
20 runs0 likes0 downloads0 reach20 impact
sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer.ColumnTransformer(num_imputer=sklearn.impute.SimpleImputer,basic_encoder=category_encoders.one_hot.OneHotEncoder),preprocessing2=sklearn.compose._column_transformer.ColumnTransformer(non_binary_imputer=__main__.SimpleImputer2),algorithm=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(num_imputer=sklearn.impute.SimpleImputer,basic_encoder=category_encoders.one_hot.OneHotEncoder) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(non_binary_imputer=__main__.SimpleImputer2) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
__main__.SimpleImputer2 (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer.ColumnTransformer(num_imputer=sklearn.impute.SimpleImputer,basic_encoder=category_encoders.one_hot.OneHotEncoder),preprocessing2=sklearn.compose._column_transformer.ColumnTransformer(non_binary_imputer=__main__.SklearnSimpleImputerCloneWithMedianStrategy),algorithm=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(non_binary_imputer=__main__.SklearnSimpleImputerCloneWithMedianStrategy) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
__main__.SklearnSimpleImputerCloneWithMedianStrategy (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(preprocessing=sklearn.compose._column_transformer.ColumnTransformer(num_imputer=sklearn.impute.SimpleImputer,basic_encoder=category_encoders.one_hot.OneHotEncoder),preprocessing2=sklearn.compose._column_transformer.ColumnTransformer(other_cat_imputer=__main__.SklearnSimpleImputerCloneWithMedianStrategy),algorithm=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(other_cat_imputer=__main__.SklearnSimpleImputerCloneWithMedianStrategy) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(imputation=sklearn.compose._column_transformer.ColumnTransformer(numeric_imputer=sklearn.impute.SimpleImputer,categorical_imputer=sklearn_clone.SklearnSimpleImputerCloneWithMedianStrategy),encoding=sklearn.compose._column_transformer.ColumnTransformer(basic_encoder=category_encoders.one_hot.OneHotEncoder),learner=sklearn.naive_bayes.GaussianNB) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(numeric_imputer=sklearn.impute.SimpleImputer,categorical_imputer=sklearn_clone.SklearnSimpleImputerCloneWithMedianStrategy) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn_clone.SklearnSimpleImputerCloneWithMedianStrategy (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.compose._column_transformer.ColumnTransformer(basic_encoder=category_encoders.one_hot.OneHotEncoder) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.naive_bayes.GaussianNB (13)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (8)
Flow generated by openml_run
55 runs0 likes5 downloads5 reach17 impact
autosklearn.estimators.AutoSklearnClassifier (2)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
tpot.tpot.TPOTClassifier (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
automlbenchmark_autosklearn (1)
Auto-sklearn as set up by the AutoML BenchmarkSource: source: https://github.com/openml/automlbenchmark/releases/tag/v0.9
26 runs0 likes0 downloads0 reach0 impact
automlbenchmark_tpot (1)
TPOT as set up by the AutoML BenchmarkSource: source: https://github.com/openml/automlbenchmark/releases/tag/v0.9
18 runs0 likes0 downloads0 reach0 impact
automlbenchmark_h2oautoml (1)
H2O AutoML 3.24.0.1 as set up by the AutoML BenchmarkSource: source: https://github.com/openml/automlbenchmark/releases/tag/v0.9
18 runs0 likes0 downloads0 reach0 impact
automlbenchmark_autoweka (1)
Auto-WEKA 2.6 as set up by the AutoML BenchmarkSource: source: https://github.com/openml/automlbenchmark/releases/tag/v0.9
17 runs0 likes1 downloads1 reach0 impact
sklearn.ensemble.forest.RandomForestClassifier (56)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
automlbenchmark_randomforest (1)
Random Forest baseline as set up by the AutoML BenchmarkSource: source: https://github.com/openml/automlbenchmark/releases/tag/v0.9
19 runs0 likes0 downloads0 reach0 impact
automlbenchmark_tunedrandomforest (1)
Tuned Random Forest baseline as set up by the AutoML BenchmarkSource: source: https://github.com/openml/automlbenchmark/releases/tag/v0.9
19 runs0 likes0 downloads0 reach0 impact
sklearn.svm.classes.SVC (41)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.naive_bayes.BernoulliNB (2)
Automatically created scikit-learn flow.
14 runs0 likes0 downloads0 reach0 impact
sklearn.naive_bayes.GaussianNB (4)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Median-Imputer=sklearn.preprocessing.imputation.Imputer,learner=sklearn.naive_bayes.BernoulliNB) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Median-Imputer=sklearn.preprocessing.imputation.Imputer,learner=sklearn.naive_bayes.GaussianNB) (1)
Automatically created scikit-learn flow.
97 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Median-Imputer=sklearn.preprocessing.imputation.Imputer,learner=sklearn.naive_bayes.MultinomialNB) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.naive_bayes.MultinomialNB (2)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Median-Imputer=sklearn.preprocessing.imputation.Imputer,learner=sklearn.tree.tree.DecisionTreeClassifier) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Median-Imputer=sklearn.preprocessing.imputation.Imputer,learner=sklearn.ensemble.forest.ExtraTreesClassifier) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble.forest.ExtraTreesClassifier (6)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Median-Imputer=sklearn.preprocessing.imputation.Imputer,learner=sklearn.ensemble.forest.RandomForestClassifier) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Median-Imputer=sklearn.preprocessing.imputation.Imputer,learner=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Median-Imputer=sklearn.preprocessing.imputation.Imputer,learner=sklearn.neighbors.classification.KNeighborsClassifier) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(Median-Imputer=sklearn.preprocessing.imputation.Imputer,learner=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
0 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier) (1)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
0 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble._forest.RandomForestClassifier (18)
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…
0 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (32)
A decision tree classifier.
0 runs0 likes0 downloads0 reach0 impact
amlb_RandomForest (1)
This flow is generated by the automl benchmark: https://github.com/openml/automlbenchmark.git Repository commit: d5c73433ffc6c57c88113a897213a6bc057e5846 RandomForest version: 1.2.2
1 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (46)
A decision tree classifier.
0 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (8)
A decision tree classifier.
0 runs0 likes0 downloads0 reach0 impact
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.impute._base.SimpleImputer (19)
Imputation transformer for completing missing values.
0 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (10)
A decision tree classifier.
1 runs0 likes0 downloads0 reach1 impact
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
1 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (15)
A decision tree classifier.
6 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (16)
A decision tree classifier.
1 runs0 likes0 downloads0 reach0 impact
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.preprocessing._data.MinMaxScaler (2)
Transform features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero…
0 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing._data.PowerTransformer (1)
Apply a power transform featurewise to make data more Gaussian-like. Power transforms are a family of parametric, monotonic transformations that are applied to make data more Gaussian-like. This is…
0 runs0 likes0 downloads0 reach0 impact
sklearn.preprocessing._data.RobustScaler (2)
Scale features using statistics that are robust to outliers. This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the…
0 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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.ensemble._forest.RandomForestClassifier (7)
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…
0 runs0 likes0 downloads0 reach0 impact
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.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)
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…
0 runs0 likes0 downloads0 reach0 impact
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…
1 runs0 likes0 downloads0 reach0 impact
sklearn.impute._base.SimpleImputer (23)
Imputation transformer for completing missing values.
0 runs0 likes0 downloads0 reach0 impact
sklearn.tree._classes.DecisionTreeClassifier (18)
A decision tree classifier.
0 runs0 likes0 downloads0 reach0 impact
amlb_constantpredictor (2)
This flow is generated by the automl benchmark: https://github.com/openml/automlbenchmark.git Repository commit: f0086d1bd6488395413bfe1f6caf8f9a34b8910d constantpredictor version: stable
3 runs0 likes0 downloads0 reach1 impact
amlb_constantpredictor (3)
This flow is generated by the automl benchmark: https://github.com/openml/automlbenchmark.git Repository commit: 75567510ce887b7b8aa857b9a1f9f29d1775813c constantpredictor version: stable
6 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) (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…
80 runs0 likes0 downloads0 reach11 impact
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder) (1)
Applies transformers to columns of an array or pandas DataFrame. This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each…
0 runs0 likes0 downloads0 reach0 impact