Run
10228900

Run 10228900

Task 14952 (Supervised Classification) PhishingWebsites Uploaded 26-06-2019 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(feature_map=sklearn_extra.kernel_approximation._f astfood.Fastfood,svm=sklearn.svm.classes.LinearSVC)(1)Automatically created scikit-learn flow.
sklearn.pipeline.Pipeline(feature_map=sklearn_extra.kernel_approximation._fastfood.Fastfood,svm=sklearn.svm.classes.LinearSVC)(1)_memorynull
sklearn.pipeline.Pipeline(feature_map=sklearn_extra.kernel_approximation._fastfood.Fastfood,svm=sklearn.svm.classes.LinearSVC)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "feature_map", "step_name": "feature_map"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "svm", "step_name": "svm"}}]
sklearn.pipeline.Pipeline(feature_map=sklearn_extra.kernel_approximation._fastfood.Fastfood,svm=sklearn.svm.classes.LinearSVC)(1)_verbosefalse
sklearn_extra.kernel_approximation._fastfood.Fastfood(1)_n_components10
sklearn_extra.kernel_approximation._fastfood.Fastfood(1)_random_state1
sklearn_extra.kernel_approximation._fastfood.Fastfood(1)_sigma1.5811388300841898
sklearn_extra.kernel_approximation._fastfood.Fastfood(1)_tradeoff_mem_accuracy"mem"
sklearn.svm.classes.LinearSVC(10)_C1.0
sklearn.svm.classes.LinearSVC(10)_class_weightnull
sklearn.svm.classes.LinearSVC(10)_dualtrue
sklearn.svm.classes.LinearSVC(10)_fit_intercepttrue
sklearn.svm.classes.LinearSVC(10)_intercept_scaling1
sklearn.svm.classes.LinearSVC(10)_loss"squared_hinge"
sklearn.svm.classes.LinearSVC(10)_max_iter1000
sklearn.svm.classes.LinearSVC(10)_multi_class"ovr"
sklearn.svm.classes.LinearSVC(10)_penalty"l2"
sklearn.svm.classes.LinearSVC(10)_random_state11598
sklearn.svm.classes.LinearSVC(10)_tol0.0001
sklearn.svm.classes.LinearSVC(10)_verbose0

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
0.751 ± 0.0104
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.758 ± 0.0103
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.508 ± 0.0209
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.5109 ± 0.0208
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.24 ± 0.0102
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4935 ± 0
Cross-validation details (10-fold Crossvalidation)
Number of instances
11055
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7599 ± 0.0104
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.76 ± 0.0102
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9906 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Recall
0.76 ± 0.0102
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4863 ± 0.0206
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4967 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4899 ± 0.0103
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9862 ± 0.0207
Cross-validation details (10-fold Crossvalidation)