Run
10160537

Run 10160537

Task 31 (Supervised Classification) credit-g Uploaded 02-04-2019 by Neeratyoy Mallik
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer, estimator=sklearn.tree.tree.DecisionTreeClassifier)(14)Automatically created scikit-learn flow.
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(14)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.tree.tree.DecisionTreeClassifier)(14)_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.preprocessing.imputation.Imputer(40)_axis0
sklearn.preprocessing.imputation.Imputer(40)_copytrue
sklearn.preprocessing.imputation.Imputer(40)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(40)_strategy"mean"
sklearn.preprocessing.imputation.Imputer(40)_verbose0
sklearn.tree.tree.DecisionTreeClassifier(38)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(38)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(38)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(38)_max_featuresnull
sklearn.tree.tree.DecisionTreeClassifier(38)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(38)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(38)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(38)_min_samples_leaf1
sklearn.tree.tree.DecisionTreeClassifier(38)_min_samples_split2
sklearn.tree.tree.DecisionTreeClassifier(38)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(38)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(38)_random_state34051
sklearn.tree.tree.DecisionTreeClassifier(38)_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.

17 Evaluation measures

Area under ROC curve
0.6467 ± 0.055
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6929 ± 0.0396
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2831 ± 0.0988
Cross-validation details (10-fold Crossvalidation)
KB relative information score
203.1725 ± 9.9792
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.312 ± 0.0391
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
0.6998 ± 0.0439
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.688 ± 0.0391
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8818
Cross-validation details (10-fold Crossvalidation)
Recall
0.688 ± 0.0391
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7426 ± 0.0931
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 ± 0.0343
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.2189 ± 0.075
Cross-validation details (10-fold Crossvalidation)