Run
10446910

Run 10446910

Task 45 (Supervised Classification) splice Uploaded 06-04-2020 by Heinrich Peters
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestcla ssifier=sklearn.ensemble.forest.RandomForestClassifier)(1)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"most_frequent"
sklearn.impute._base.SimpleImputer(1)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(11)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(11)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(11)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(11)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(11)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(11)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(11)_sparsetrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_verbosefalse
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_features0.7583040407073237
sklearn.ensemble.forest.RandomForestClassifier(63)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(63)_min_impurity_decrease1e-07
sklearn.ensemble.forest.RandomForestClassifier(63)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(63)_min_samples_leaf20
sklearn.ensemble.forest.RandomForestClassifier(63)_min_samples_split20
sklearn.ensemble.forest.RandomForestClassifier(63)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(63)_n_estimators100
sklearn.ensemble.forest.RandomForestClassifier(63)_n_jobs1
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

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
0.9897 ± 0.0039
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9462 ± 0.0129
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9126 ± 0.0207
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8577 ± 0.0161
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0747 ± 0.0069
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4101 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9458 ± 0.0131
Cross-validation details (10-fold Crossvalidation)
Number of instances
3190
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9473 ± 0.0122
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9458 ± 0.0131
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.4802 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1822 ± 0.0168
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4528 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1708 ± 0.0167
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3772 ± 0.0369
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9454 ± 0.0122
Cross-validation details (10-fold Crossvalidation)