OpenML
5988637

Run 5988637

Task 36 (Supervised Classification) segment Uploaded 17-07-2017 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)_sparsefalse
sklearn.feature_selection.variance_threshold.VarianceThreshold(4)_threshold0.0
sklearn.svm.classes.SVC(8)_C23.206404708247746
sklearn.svm.classes.SVC(8)_cache_size200
sklearn.svm.classes.SVC(8)_class_weightnull
sklearn.svm.classes.SVC(8)_coef00.8873255142130732
sklearn.svm.classes.SVC(8)_decision_function_shapenull
sklearn.svm.classes.SVC(8)_degree5
sklearn.svm.classes.SVC(8)_gamma0.000657454516507563
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_state9096
sklearn.svm.classes.SVC(8)_shrinkingfalse
sklearn.svm.classes.SVC(8)_tol0.0008823359248527961
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
0.9844 ± 0.0028
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8427 ± 0.0263
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8404 ± 0.0189
Cross-validation details (10-fold Crossvalidation)
KB relative information score
1884.665 ± 2.9507
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0708 ± 0.0049
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2449
Cross-validation details (10-fold Crossvalidation)
Number of instances
2310
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8961 ± 0.0451
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8632 ± 0.0162
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.8074
Cross-validation details (10-fold Crossvalidation)
Recall
0.8632 ± 0.0162
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2891 ± 0.02
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3499
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.166 ± 0.0044
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4745 ± 0.0127
Cross-validation details (10-fold Crossvalidation)