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268

Run 268

Task 96 (Supervised Classification) credit-a Uploaded 29-10-2019 by Continuous Integration
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  • openml-python Sklearn_0.19.2. study_35
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Flow

TEST74519dd4b9sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imput ation.Imputer,VarianceThreshold=sklearn.feature_selection.variance_threshol d.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSea rchCV(estimator=sklearn.tree.tree.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 and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting to None.
TEST74519dd4b9sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier))(1)_memorynull
TEST74519dd4b9sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier))(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "VarianceThreshold", "step_name": "VarianceThreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "Estimator", "step_name": "Estimator"}}]
TEST74519dd4b9sklearn.preprocessing.imputation.Imputer(1)_axis0
TEST74519dd4b9sklearn.preprocessing.imputation.Imputer(1)_copytrue
TEST74519dd4b9sklearn.preprocessing.imputation.Imputer(1)_missing_values"NaN"
TEST74519dd4b9sklearn.preprocessing.imputation.Imputer(1)_strategy"median"
TEST74519dd4b9sklearn.preprocessing.imputation.Imputer(1)_verbose0
TEST74519dd4b9sklearn.feature_selection.variance_threshold.VarianceThreshold(1)_threshold0.0
TEST74519dd4b9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_cv3
TEST74519dd4b9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_error_score"raise"
TEST74519dd4b9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_fit_paramsnull
TEST74519dd4b9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_iidtrue
TEST74519dd4b9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_n_iter10
TEST74519dd4b9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_n_jobs1
TEST74519dd4b9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_param_distributions{"min_samples_leaf": [1, 2, 4, 8, 16, 32, 64], "min_samples_split": [2, 4, 8, 16, 32, 64, 128]}
TEST74519dd4b9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_pre_dispatch"2*n_jobs"
TEST74519dd4b9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_random_state33003
TEST74519dd4b9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_refittrue
TEST74519dd4b9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_return_train_score"warn"
TEST74519dd4b9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_scoringnull
TEST74519dd4b9sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_verbose0
TEST74519dd4b9sklearn.tree.tree.DecisionTreeClassifier(1)_class_weightnull
TEST74519dd4b9sklearn.tree.tree.DecisionTreeClassifier(1)_criterion"gini"
TEST74519dd4b9sklearn.tree.tree.DecisionTreeClassifier(1)_max_depthnull
TEST74519dd4b9sklearn.tree.tree.DecisionTreeClassifier(1)_max_featuresnull
TEST74519dd4b9sklearn.tree.tree.DecisionTreeClassifier(1)_max_leaf_nodesnull
TEST74519dd4b9sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TEST74519dd4b9sklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_splitnull
TEST74519dd4b9sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_leaf1
TEST74519dd4b9sklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_split2
TEST74519dd4b9sklearn.tree.tree.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TEST74519dd4b9sklearn.tree.tree.DecisionTreeClassifier(1)_presortfalse
TEST74519dd4b9sklearn.tree.tree.DecisionTreeClassifier(1)_random_state62501
TEST74519dd4b9sklearn.tree.tree.DecisionTreeClassifier(1)_splitter"best"

Result files

xml
Description

XML file describing the run, including user-defined evaluation measures.

arff
Predictions

ARFF file with instance-level predictions generated by the model.

18 Evaluation measures

0.9125
Per class
Cross-validation details (33% Holdout set)
0.7918
Per class
Cross-validation details (33% Holdout set)
0.5834
Cross-validation details (33% Holdout set)
0.5977
Cross-validation details (33% Holdout set)
0.2084
Cross-validation details (33% Holdout set)
0.4978
Cross-validation details (33% Holdout set)
0.793
Cross-validation details (33% Holdout set)
227
Per class
Cross-validation details (33% Holdout set)
0.7959
Per class
Cross-validation details (33% Holdout set)
0.793
Cross-validation details (33% Holdout set)
1.0024
Cross-validation details (33% Holdout set)
0.4186
Cross-validation details (33% Holdout set)
0.5008
Cross-validation details (33% Holdout set)
0.3397
Cross-validation details (33% Holdout set)
0.6783
Cross-validation details (33% Holdout set)
0.7904
Cross-validation details (33% Holdout set)