Run
10560434

Run 10560434

Task 14952 (Supervised Classification) PhishingWebsites Uploaded 14-08-2021 by Sergey Redyuk
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.Standar dScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression )(5)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting to None.
sklearn.linear_model.logistic.LogisticRegression(36)_C1.0
sklearn.linear_model.logistic.LogisticRegression(36)_class_weightnull
sklearn.linear_model.logistic.LogisticRegression(36)_dualfalse
sklearn.linear_model.logistic.LogisticRegression(36)_fit_intercepttrue
sklearn.linear_model.logistic.LogisticRegression(36)_intercept_scaling1
sklearn.linear_model.logistic.LogisticRegression(36)_max_iter100
sklearn.linear_model.logistic.LogisticRegression(36)_multi_class"ovr"
sklearn.linear_model.logistic.LogisticRegression(36)_n_jobs1
sklearn.linear_model.logistic.LogisticRegression(36)_penalty"l2"
sklearn.linear_model.logistic.LogisticRegression(36)_random_state1550
sklearn.linear_model.logistic.LogisticRegression(36)_solver"liblinear"
sklearn.linear_model.logistic.LogisticRegression(36)_tol0.0001
sklearn.linear_model.logistic.LogisticRegression(36)_verbose0
sklearn.linear_model.logistic.LogisticRegression(36)_warm_startfalse
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression)(5)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "logisticregression", "step_name": "logisticregression"}}]
sklearn.preprocessing.data.StandardScaler(43)_copytrue
sklearn.preprocessing.data.StandardScaler(43)_with_meantrue
sklearn.preprocessing.data.StandardScaler(43)_with_stdtrue

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.

18 Evaluation measures

Area under ROC curve
0.9788 ± 0.0031
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9267 ± 0.0063
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8513 ± 0.0127
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.7947 ± 0.0113
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1083 ± 0.0055
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4935 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9268 ± 0.0062
Cross-validation details (10-fold Crossvalidation)
Number of instances
11055
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9268 ± 0.0063
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9268 ± 0.0062
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9906 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.2195 ± 0.0112
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4967 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2333 ± 0.0082
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4696 ± 0.0165
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9246 ± 0.0066
Cross-validation details (10-fold Crossvalidation)