Run
18056

Run 18056

Task 96 (Supervised Classification) credit-a Uploaded 12-11-2019 by Continuous Integration
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  • openml-python Sklearn_0.22.dev0.
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Flow

TESTeef761ee8esklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.Simple Imputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.Var ianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV (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 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 it to 'passthrough' or ``None``.
TESTeef761ee8esklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier))(1)_memorynull
TESTeef761ee8esklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.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"}}]
TESTeef761ee8esklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier))(1)_verbosefalse
TESTeef761ee8esklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TESTeef761ee8esklearn.impute._base.SimpleImputer(1)_copytrue
TESTeef761ee8esklearn.impute._base.SimpleImputer(1)_fill_valuenull
TESTeef761ee8esklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TESTeef761ee8esklearn.impute._base.SimpleImputer(1)_strategy"median"
TESTeef761ee8esklearn.impute._base.SimpleImputer(1)_verbose0
TESTeef761ee8esklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold0.0
TESTeef761ee8esklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_cv3
TESTeef761ee8esklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_error_scoreNaN
TESTeef761ee8esklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_iid"deprecated"
TESTeef761ee8esklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_iter10
TESTeef761ee8esklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_jobsnull
TESTeef761ee8esklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_param_distributions{"min_samples_leaf": [1, 2, 4, 8, 16, 32, 64], "min_samples_split": [2, 4, 8, 16, 32, 64, 128]}
TESTeef761ee8esklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_pre_dispatch"2*n_jobs"
TESTeef761ee8esklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_random_state33003
TESTeef761ee8esklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_refittrue
TESTeef761ee8esklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_return_train_scorefalse
TESTeef761ee8esklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_scoringnull
TESTeef761ee8esklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose0
TESTeef761ee8esklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha0.0
TESTeef761ee8esklearn.tree._classes.DecisionTreeClassifier(1)_class_weightnull
TESTeef761ee8esklearn.tree._classes.DecisionTreeClassifier(1)_criterion"gini"
TESTeef761ee8esklearn.tree._classes.DecisionTreeClassifier(1)_max_depthnull
TESTeef761ee8esklearn.tree._classes.DecisionTreeClassifier(1)_max_featuresnull
TESTeef761ee8esklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodesnull
TESTeef761ee8esklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TESTeef761ee8esklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_splitnull
TESTeef761ee8esklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf1
TESTeef761ee8esklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split2
TESTeef761ee8esklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TESTeef761ee8esklearn.tree._classes.DecisionTreeClassifier(1)_presort"deprecated"
TESTeef761ee8esklearn.tree._classes.DecisionTreeClassifier(1)_random_state62501
TESTeef761ee8esklearn.tree._classes.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)