Run
10228923

Run 10228923

Task 31 (Supervised Classification) credit-g Uploaded 30-06-2019 by Juliano Garcia
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=sk learn.tree.tree.DecisionTreeClassifier)(4)Automatically created scikit-learn flow.
sklearn.tree.tree.DecisionTreeClassifier(42)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(42)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(42)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(42)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(42)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(42)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(42)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(42)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(42)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(42)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(42)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(42)_random_state53122
sklearn.tree.tree.DecisionTreeClassifier(42)_splitter"best"
sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(4)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute.SimpleImputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(4)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.impute.SimpleImputer(12)_copytrue
sklearn.impute.SimpleImputer(12)_fill_valuenull
sklearn.impute.SimpleImputer(12)_missing_valuesNaN
sklearn.impute.SimpleImputer(12)_strategy"mean"
sklearn.impute.SimpleImputer(12)_verbose0

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.

17 Evaluation measures

Area under ROC curve
0.641 ± 0.0538
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6915 ± 0.0426
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2751 ± 0.0995
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2033 ± 0.1141
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.312 ± 0.0447
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4202
Cross-validation details (10-fold Crossvalidation)
Number of instances
1000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.696 ± 0.0431
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.688 ± 0.0447
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8813
Cross-validation details (10-fold Crossvalidation)
Recall
0.688 ± 0.0447
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7426 ± 0.1063
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4583
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.5586 ± 0.0399
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.2189 ± 0.087
Cross-validation details (10-fold Crossvalidation)