Run
156

Run 156

Task 96 (Supervised Classification) credit-a Uploaded 29-10-2019 by Continuous Integration
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • openml-python Sklearn_0.21.2. study_35
Issue #Downvotes for this reason By


Flow

TESTa4808aba2csklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.Simple Imputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.Vari anceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV( 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 it to 'passthrough' or ``None``.
TESTa4808aba2csklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier))(1)_memorynull
TESTa4808aba2csklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,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"}}]
TESTa4808aba2csklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier))(1)_verbosefalse
TESTa4808aba2csklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TESTa4808aba2csklearn.impute._base.SimpleImputer(1)_copytrue
TESTa4808aba2csklearn.impute._base.SimpleImputer(1)_fill_valuenull
TESTa4808aba2csklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TESTa4808aba2csklearn.impute._base.SimpleImputer(1)_strategy"median"
TESTa4808aba2csklearn.impute._base.SimpleImputer(1)_verbose0
TESTa4808aba2csklearn.feature_selection.variance_threshold.VarianceThreshold(1)_threshold0.0
TESTa4808aba2csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_cv3
TESTa4808aba2csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_error_score"raise-deprecating"
TESTa4808aba2csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_iid"warn"
TESTa4808aba2csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_n_iter10
TESTa4808aba2csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_n_jobsnull
TESTa4808aba2csklearn.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]}
TESTa4808aba2csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_pre_dispatch"2*n_jobs"
TESTa4808aba2csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_random_state33003
TESTa4808aba2csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_refittrue
TESTa4808aba2csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_return_train_scorefalse
TESTa4808aba2csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_scoringnull
TESTa4808aba2csklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.tree.tree.DecisionTreeClassifier)(1)_verbose0
TESTa4808aba2csklearn.tree.tree.DecisionTreeClassifier(1)_class_weightnull
TESTa4808aba2csklearn.tree.tree.DecisionTreeClassifier(1)_criterion"gini"
TESTa4808aba2csklearn.tree.tree.DecisionTreeClassifier(1)_max_depthnull
TESTa4808aba2csklearn.tree.tree.DecisionTreeClassifier(1)_max_featuresnull
TESTa4808aba2csklearn.tree.tree.DecisionTreeClassifier(1)_max_leaf_nodesnull
TESTa4808aba2csklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_decrease0.0
TESTa4808aba2csklearn.tree.tree.DecisionTreeClassifier(1)_min_impurity_splitnull
TESTa4808aba2csklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_leaf1
TESTa4808aba2csklearn.tree.tree.DecisionTreeClassifier(1)_min_samples_split2
TESTa4808aba2csklearn.tree.tree.DecisionTreeClassifier(1)_min_weight_fraction_leaf0.0
TESTa4808aba2csklearn.tree.tree.DecisionTreeClassifier(1)_presortfalse
TESTa4808aba2csklearn.tree.tree.DecisionTreeClassifier(1)_random_state62501
TESTa4808aba2csklearn.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)