Run
10462258

Run 10462258

Task 14952 (Supervised Classification) PhishingWebsites Uploaded 21-05-2020 by Heinrich Peters
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Flow

sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer, onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestcla ssifier=sklearn.ensemble.forest.RandomForestClassifier)(2)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 transformers in the pipeline can be cached using ``memory`` argument. 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 it to 'passthrough' or ``None``.
sklearn.impute._base.SimpleImputer(11)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(11)_copytrue
sklearn.impute._base.SimpleImputer(11)_fill_valuenull
sklearn.impute._base.SimpleImputer(11)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(11)_strategy"most_frequent"
sklearn.impute._base.SimpleImputer(11)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(16)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(16)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(16)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(16)_sparsetrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_memorynull
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2)_verbosefalse
sklearn.ensemble.forest.RandomForestClassifier(64)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(64)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(64)_criterion"entropy"
sklearn.ensemble.forest.RandomForestClassifier(64)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(64)_max_features0.9196933972221343
sklearn.ensemble.forest.RandomForestClassifier(64)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(64)_min_impurity_decrease0
sklearn.ensemble.forest.RandomForestClassifier(64)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(64)_min_samples_leaf19
sklearn.ensemble.forest.RandomForestClassifier(64)_min_samples_split18
sklearn.ensemble.forest.RandomForestClassifier(64)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(64)_n_estimators100
sklearn.ensemble.forest.RandomForestClassifier(64)_n_jobsnull
sklearn.ensemble.forest.RandomForestClassifier(64)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(64)_random_state1
sklearn.ensemble.forest.RandomForestClassifier(64)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(64)_warm_startfalse

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
0.99 ± 0.0019
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.944 ± 0.0057
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.8865 ± 0.0116
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8356 ± 0.0101
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0862 ± 0.005
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4935 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.944 ± 0.0057
Cross-validation details (10-fold Crossvalidation)
Number of instances
11055
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.944 ± 0.0057
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.944 ± 0.0057
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9906 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1746 ± 0.01
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4967 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1991 ± 0.0076
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.4008 ± 0.0154
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.943 ± 0.006
Cross-validation details (10-fold Crossvalidation)