OpenML
5983801

Run 5983801

Task 219 (Supervised Classification) electricity 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[1]
openmlstudy14.preprocessing.ConditionalImputer(2)_copytrue
openmlstudy14.preprocessing.ConditionalImputer(2)_fill_empty0
openmlstudy14.preprocessing.ConditionalImputer(2)_missing_values"NaN"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(2)_strategy_nominal"most_frequent"
openmlstudy14.preprocessing.ConditionalImputer(2)_verbose0
sklearn.preprocessing.data.OneHotEncoder(7)_categorical_features[1]
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)_C4.650843122322527
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)_gamma0.004576716146736878
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_state45429
sklearn.svm.classes.SVC(8)_shrinkingfalse
sklearn.svm.classes.SVC(8)_tol0.0036364908418013227
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
0.786 ± 0.0086
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7134 ± 0.0091
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4101 ± 0.0188
Cross-validation details (10-fold Crossvalidation)
KB relative information score
11901.4299 ± 41.3122
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.372 ± 0.0038
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4886 ± 0
Cross-validation details (10-fold Crossvalidation)
Number of instances
45312
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7155 ± 0.009
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7176 ± 0.0087
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9835
Cross-validation details (10-fold Crossvalidation)
Recall
0.7176 ± 0.0087
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7613 ± 0.0078
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4943 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4298 ± 0.0041
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8696 ± 0.0083
Cross-validation details (10-fold Crossvalidation)