Run
10529064

Run 10529064

Task 9976 (Supervised Classification) madelon Uploaded 06-08-2020 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression =sklearn.linear_model.logistic.LogisticRegression)(2)Automatically created scikit-learn flow.
sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(1)_copytrue
sklearn.impute._base.SimpleImputer(1)_fill_valuenull
sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(1)_strategy"median"
sklearn.impute._base.SimpleImputer(1)_verbose0
sklearn.linear_model.logistic.LogisticRegression(26)_C224.662208931
sklearn.linear_model.logistic.LogisticRegression(26)_class_weightnull
sklearn.linear_model.logistic.LogisticRegression(26)_dualtrue
sklearn.linear_model.logistic.LogisticRegression(26)_fit_intercepttrue
sklearn.linear_model.logistic.LogisticRegression(26)_intercept_scaling1
sklearn.linear_model.logistic.LogisticRegression(26)_l1_rationull
sklearn.linear_model.logistic.LogisticRegression(26)_max_iter785
sklearn.linear_model.logistic.LogisticRegression(26)_multi_class"warn"
sklearn.linear_model.logistic.LogisticRegression(26)_n_jobsnull
sklearn.linear_model.logistic.LogisticRegression(26)_penalty"l2"
sklearn.linear_model.logistic.LogisticRegression(26)_random_state1
sklearn.linear_model.logistic.LogisticRegression(26)_solver"liblinear"
sklearn.linear_model.logistic.LogisticRegression(26)_tol0.0001
sklearn.linear_model.logistic.LogisticRegression(26)_verbose0
sklearn.linear_model.logistic.LogisticRegression(26)_warm_startfalse
sklearn.preprocessing.data.StandardScaler(29)_copytrue
sklearn.preprocessing.data.StandardScaler(29)_with_meantrue
sklearn.preprocessing.data.StandardScaler(29)_with_stdtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression)(2)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "logisticregression", "step_name": "logisticregression"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression)(2)_verbosefalse

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

Area under ROC curve
0.5602 ± 0.0398
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.545 ± 0.0298
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.09 ± 0.0595
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0787 ± 0.0595
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4614 ± 0.0292
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.5
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.545 ± 0.0297
Cross-validation details (10-fold Crossvalidation)
Number of instances
2600
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.545 ± 0.0299
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.545 ± 0.0297
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9228 ± 0.0584
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.5
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.6118 ± 0.0258
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.2236 ± 0.0516
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.545 ± 0.0297
Cross-validation details (10-fold Crossvalidation)