OpenML
5025429

Run 5025429

Task 9957 (Supervised Classification) qsar-biodeg Uploaded 09-07-2017 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"median"
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)_C29.02151045190776
sklearn.svm.classes.SVC(8)_cache_size200
sklearn.svm.classes.SVC(8)_class_weightnull
sklearn.svm.classes.SVC(8)_coef00.0
sklearn.svm.classes.SVC(8)_decision_function_shapenull
sklearn.svm.classes.SVC(8)_degree3
sklearn.svm.classes.SVC(8)_gamma0.37989439723511587
sklearn.svm.classes.SVC(8)_kernel"rbf"
sklearn.svm.classes.SVC(8)_max_iter-1
sklearn.svm.classes.SVC(8)_probabilitytrue
sklearn.svm.classes.SVC(8)_random_state27302
sklearn.svm.classes.SVC(8)_shrinkingfalse
sklearn.svm.classes.SVC(8)_tol7.352007412126074e-05
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
0.8358 ± 0.0504
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7756 ± 0.0674
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4842 ± 0.1523
Cross-validation details (10-fold Crossvalidation)
KB relative information score
362.4489 ± 8.0489
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3054 ± 0.0293
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4472 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
Number of instances
1055
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.798 ± 0.062
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7924 ± 0.0541
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9226
Cross-validation details (10-fold Crossvalidation)
Recall
0.7924 ± 0.0541
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6829 ± 0.0652
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4728 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3879 ± 0.0368
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8203 ± 0.0774
Cross-validation details (10-fold Crossvalidation)