Run
8831582

Run 8831582

Task 3889 (Supervised Classification) sylva_agnostic Uploaded 25-01-2018 by Jan van Rijn
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  • openml-pimp openml-python Sklearn_0.18.1.
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Flow

sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.Conditiona lImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklea rn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_sele ction.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.S VC)(1)Automatically created scikit-learn flow.
openmlstudy14.preprocessing.ConditionalImputer(6)_axis0
openmlstudy14.preprocessing.ConditionalImputer(6)_categorical_features[]
openmlstudy14.preprocessing.ConditionalImputer(6)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(6)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(6)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(6)_strategy"median"
openmlstudy14.preprocessing.ConditionalImputer(6)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(6)_verbose0
sklearn.preprocessing.data.OneHotEncoder(18)_categorical_features[]
sklearn.preprocessing.data.OneHotEncoder(18)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(18)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(18)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(18)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(12)_threshold0.0
sklearn.preprocessing.data.StandardScaler(6)_copytrue
sklearn.preprocessing.data.StandardScaler(6)_with_meanfalse
sklearn.preprocessing.data.StandardScaler(6)_with_stdtrue
sklearn.svm.classes.SVC(17)_C0.175323768348567
sklearn.svm.classes.SVC(17)_cache_size200
sklearn.svm.classes.SVC(17)_class_weightnull
sklearn.svm.classes.SVC(17)_coef00.05037329066859697
sklearn.svm.classes.SVC(17)_decision_function_shapenull
sklearn.svm.classes.SVC(17)_degree2
sklearn.svm.classes.SVC(17)_gamma0.0003939567086576481
sklearn.svm.classes.SVC(17)_kernel"poly"
sklearn.svm.classes.SVC(17)_max_iter-1
sklearn.svm.classes.SVC(17)_probabilitytrue
sklearn.svm.classes.SVC(17)_random_state3627
sklearn.svm.classes.SVC(17)_shrinkingtrue
sklearn.svm.classes.SVC(17)_tol0.002231469438219151
sklearn.svm.classes.SVC(17)_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.

17 Evaluation measures

Area under ROC curve
0.9979 ± 0.0011
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9884 ± 0.0022
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9016 ± 0.0187
Cross-validation details (10-fold Crossvalidation)
KB relative information score
11152.5221 ± 54.4358
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0164 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1156 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Number of instances
14395
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9888 ± 0.0021
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9882 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.3338
Cross-validation details (10-fold Crossvalidation)
Recall
0.9882 ± 0.0023
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1417 ± 0.0198
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2403 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.094 ± 0.0086
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3912 ± 0.0359
Cross-validation details (10-fold Crossvalidation)