Run
6208686

Run 6208686

Task 9950 (Supervised Classification) micro-mass Uploaded 27-08-2017 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,variencethres hold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classif ier=sklearn.svm.classes.SVC)(1)Automatically created scikit-learn flow.
openmlstudy14.preprocessing.ConditionalImputer(2)_axis0
openmlstudy14.preprocessing.ConditionalImputer(2)_categorical_features[]
openmlstudy14.preprocessing.ConditionalImputer(2)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(2)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(2)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy"mean"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(2)_verbose0
sklearn.preprocessing.data.OneHotEncoder(7)_categorical_features[]
sklearn.preprocessing.data.OneHotEncoder(7)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing.data.OneHotEncoder(7)_handle_unknown"ignore"
sklearn.preprocessing.data.OneHotEncoder(7)_n_values"auto"
sklearn.preprocessing.data.OneHotEncoder(7)_sparsetrue
sklearn.feature_selection.variance_threshold.VarianceThreshold(4)_threshold0.0
sklearn.svm.classes.SVC(8)_C23.383964179742126
sklearn.svm.classes.SVC(8)_cache_size200
sklearn.svm.classes.SVC(8)_class_weightnull
sklearn.svm.classes.SVC(8)_coef0-0.680832307893372
sklearn.svm.classes.SVC(8)_decision_function_shapenull
sklearn.svm.classes.SVC(8)_degree1
sklearn.svm.classes.SVC(8)_gamma0.02403891680172721
sklearn.svm.classes.SVC(8)_kernel"poly"
sklearn.svm.classes.SVC(8)_max_iter-1
sklearn.svm.classes.SVC(8)_probabilitytrue
sklearn.svm.classes.SVC(8)_random_state14952
sklearn.svm.classes.SVC(8)_shrinkingfalse
sklearn.svm.classes.SVC(8)_tol0.00019929763110201638
sklearn.svm.classes.SVC(8)_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
0.9794 ± 0.0064
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7194 ± 0.0282
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6999 ± 0.0889
Cross-validation details (10-fold Crossvalidation)
KB relative information score
363.0344 ± 2.6763
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0555 ± 0.0042
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.0941 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Number of instances
571
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.761 ± 0.0275
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7198 ± 0.0818
Cross-validation details (10-fold Crossvalidation)
Prior entropy
4.208
Cross-validation details (10-fold Crossvalidation)
Recall
0.7198 ± 0.0818
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.5893 ± 0.0445
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2169 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1517 ± 0.009
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6993 ± 0.0412
Cross-validation details (10-fold Crossvalidation)