Run
8815188

Run 8815188

Task 23 (Supervised Classification) cmc Uploaded 24-01-2018 by Jan van Rijn
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  • openml-pimp openml-python Sklearn_0.18.1. study_71
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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[1, 2, 4, 5, 6, 7, 8]
openmlstudy14.preprocessing.ConditionalImputer(6)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(6)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(6)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(6)_strategy"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(6)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(6)_verbose0
sklearn.preprocessing.data.OneHotEncoder(18)_categorical_features[1, 2, 4, 5, 6, 7, 8]
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)_C0.25315983013770665
sklearn.svm.classes.SVC(17)_cache_size200
sklearn.svm.classes.SVC(17)_class_weightnull
sklearn.svm.classes.SVC(17)_coef0-0.42734584482133897
sklearn.svm.classes.SVC(17)_decision_function_shapenull
sklearn.svm.classes.SVC(17)_degree4
sklearn.svm.classes.SVC(17)_gamma0.551158529807373
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_state27955
sklearn.svm.classes.SVC(17)_shrinkingfalse
sklearn.svm.classes.SVC(17)_tol0.000661462394683565
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
0.6183 ± 0.0334
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.3112 ± 0.0314
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0007 ± 0.0405
Cross-validation details (10-fold Crossvalidation)
KB relative information score
66.9849 ± 1.0628
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4212 ± 0.0022
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4308 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Number of instances
1473
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.3097 ± 0.0305
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.4162 ± 0.0237
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.5392
Cross-validation details (10-fold Crossvalidation)
Recall
0.4162 ± 0.0237
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9776 ± 0.0049
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4641 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4577 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9862 ± 0.0037
Cross-validation details (10-fold Crossvalidation)