Run
17990

Run 17990

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

TESTbff6babca2sklearn.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``.
TESTbff6babca2sklearn.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
TESTbff6babca2sklearn.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"}}]
TESTbff6babca2sklearn.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
TESTbff6babca2sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TESTbff6babca2sklearn.impute._base.SimpleImputer(1)_copytrue
TESTbff6babca2sklearn.impute._base.SimpleImputer(1)_fill_valuenull
TESTbff6babca2sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TESTbff6babca2sklearn.impute._base.SimpleImputer(1)_strategy"median"
TESTbff6babca2sklearn.impute._base.SimpleImputer(1)_verbose0
TESTbff6babca2sklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold0.0
TESTbff6babca2sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_cv3
TESTbff6babca2sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_error_scoreNaN
TESTbff6babca2sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_iid"deprecated"
TESTbff6babca2sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_iter10
TESTbff6babca2sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_n_jobsnull
TESTbff6babca2sklearn.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]}
TESTbff6babca2sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_pre_dispatch"2*n_jobs"
TESTbff6babca2sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_random_state33003
TESTbff6babca2sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_refittrue
TESTbff6babca2sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_return_train_scorefalse
TESTbff6babca2sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_scoringnull
TESTbff6babca2sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree._classes.DecisionTreeClassifier)(1)_verbose0
TESTbff6babca2sklearn.tree._classes.DecisionTreeClassifier(1)_ccp_alpha0.0
TESTbff6babca2sklearn.tree._classes.DecisionTreeClassifier(1)_class_weightnull
TESTbff6babca2sklearn.tree._classes.DecisionTreeClassifier(1)_criterion"gini"
TESTbff6babca2sklearn.tree._classes.DecisionTreeClassifier(1)_max_depthnull
TESTbff6babca2sklearn.tree._classes.DecisionTreeClassifier(1)_max_featuresnull
TESTbff6babca2sklearn.tree._classes.DecisionTreeClassifier(1)_max_leaf_nodesnull
TESTbff6babca2sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TESTbff6babca2sklearn.tree._classes.DecisionTreeClassifier(1)_min_impurity_splitnull
TESTbff6babca2sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_leaf1
TESTbff6babca2sklearn.tree._classes.DecisionTreeClassifier(1)_min_samples_split2
TESTbff6babca2sklearn.tree._classes.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TESTbff6babca2sklearn.tree._classes.DecisionTreeClassifier(1)_presort"deprecated"
TESTbff6babca2sklearn.tree._classes.DecisionTreeClassifier(1)_random_state62501
TESTbff6babca2sklearn.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)