Run
9273236

Run 9273236

Task 34537 (Supervised Classification) PhishingWebsites Uploaded 09-10-2018 by Jan van Rijn
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transfo rmer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=s klearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imp uter,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=skle arn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotenco der=sklearn.preprocessing._encoders.OneHotEncoder)),mlpclassifier=sklearn.n eural_network.multilayer_perceptron.MLPClassifier)(1)Automatically created scikit-learn flow.
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_n_jobsnull
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_remainder"passthrough"
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_sparse_threshold0.3
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))(1)_transformer_weightsnull
sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(1)_memorynull
sklearn.impute.MissingIndicator(1)_error_on_newfalse
sklearn.impute.MissingIndicator(1)_features"missing-only"
sklearn.impute.MissingIndicator(1)_missing_valuesNaN
sklearn.impute.MissingIndicator(1)_sparse"auto"
sklearn.preprocessing.imputation.Imputer(29)_axis0
sklearn.preprocessing.imputation.Imputer(29)_copytrue
sklearn.preprocessing.imputation.Imputer(29)_missing_values"NaN"
sklearn.preprocessing.imputation.Imputer(29)_strategy"mean"
sklearn.preprocessing.imputation.Imputer(29)_verbose0
sklearn.preprocessing.data.StandardScaler(14)_copytrue
sklearn.preprocessing.data.StandardScaler(14)_with_meantrue
sklearn.preprocessing.data.StandardScaler(14)_with_stdtrue
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memorynull
sklearn.impute.SimpleImputer(1)_copytrue
sklearn.impute.SimpleImputer(1)_fill_value-1
sklearn.impute.SimpleImputer(1)_missing_valuesNaN
sklearn.impute.SimpleImputer(1)_strategy"constant"
sklearn.impute.SimpleImputer(1)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(3)_categorical_featuresnull
sklearn.preprocessing._encoders.OneHotEncoder(3)_categoriesnull
sklearn.preprocessing._encoders.OneHotEncoder(3)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(3)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(3)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(3)_sparsetrue
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(missingindicator=sklearn.impute.MissingIndicator,imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),mlpclassifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier)(1)_memorynull
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_activation"logistic"
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_alpha0.005427691243963123
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_batch_size517
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_beta_10.9
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_beta_20.999
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_early_stoppingtrue
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_epsilon1e-08
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_hidden_layer_sizes717
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_learning_rate"constant"
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_learning_rate_init0.00025043990198212997
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_max_iter766
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_momentum0.15182107658245791
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_n_iter_no_change803
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_nesterovs_momentumtrue
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_power_t0.03842131678544927
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_random_state20854
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_shuffletrue
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_solver"sgd"
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_tol0.01722955412681105
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_validation_fraction0.8666621310467058
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_verbosefalse
sklearn.neural_network.multilayer_perceptron.MLPClassifier(15)_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.

15 Evaluation measures

Area under ROC curve
0.3929 ± 0.0146
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-1.3178 ± 0.4316
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4935 ± 0.0002
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)
Predictive accuracy
0.5569 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9906
Cross-validation details (10-fold Crossvalidation)
Recall
0.5569 ± 0.0003
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4967 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4979 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.0023 ± 0.0003
Cross-validation details (10-fold Crossvalidation)