Run
10446889

Run 10446889

Task 146800 (Supervised Classification) MiceProtein Uploaded 06-04-2020 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassi fier=sklearn.ensemble.forest.RandomForestClassifier)(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.preprocessing.data.StandardScaler(29)_copytrue
sklearn.preprocessing.data.StandardScaler(29)_with_meantrue
sklearn.preprocessing.data.StandardScaler(29)_with_stdtrue
sklearn.ensemble.forest.RandomForestClassifier(63)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(63)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(63)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(63)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(63)_max_features"auto"
sklearn.ensemble.forest.RandomForestClassifier(63)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(63)_min_impurity_decrease0.1
sklearn.ensemble.forest.RandomForestClassifier(63)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(63)_min_samples_leaf1
sklearn.ensemble.forest.RandomForestClassifier(63)_min_samples_split2
sklearn.ensemble.forest.RandomForestClassifier(63)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(63)_n_estimators500
sklearn.ensemble.forest.RandomForestClassifier(63)_n_jobs-1
sklearn.ensemble.forest.RandomForestClassifier(63)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(63)_random_state1
sklearn.ensemble.forest.RandomForestClassifier(63)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(63)_warm_startfalse
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(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": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(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.

16 Evaluation measures

Area under ROC curve
0.8133 ± 0.0063
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.1548 ± 0.0075
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0513 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2149 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2185 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.2722 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
Number of instances
1080
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.2722 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.993 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9839 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3305 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3255 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9847 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.245 ± 0.0058
Cross-validation details (10-fold Crossvalidation)