Run
9535439

Run 9535439

Task 20 (Supervised Classification) mfeat-pixel Uploaded 11-10-2018 by Jan van Rijn
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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)),extratreesclassifier=sk learn.ensemble.forest.ExtraTreesClassifier)(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"median"
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)),extratreesclassifier=sklearn.ensemble.forest.ExtraTreesClassifier)(1)_memorynull
sklearn.ensemble.forest.ExtraTreesClassifier(10)_bootstrapfalse
sklearn.ensemble.forest.ExtraTreesClassifier(10)_class_weightnull
sklearn.ensemble.forest.ExtraTreesClassifier(10)_criterion"entropy"
sklearn.ensemble.forest.ExtraTreesClassifier(10)_max_depthnull
sklearn.ensemble.forest.ExtraTreesClassifier(10)_max_features0.4250936605063027
sklearn.ensemble.forest.ExtraTreesClassifier(10)_max_leaf_nodesnull
sklearn.ensemble.forest.ExtraTreesClassifier(10)_min_impurity_decrease0.0
sklearn.ensemble.forest.ExtraTreesClassifier(10)_min_impurity_splitnull
sklearn.ensemble.forest.ExtraTreesClassifier(10)_min_samples_leaf5
sklearn.ensemble.forest.ExtraTreesClassifier(10)_min_samples_split3
sklearn.ensemble.forest.ExtraTreesClassifier(10)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.ExtraTreesClassifier(10)_n_estimators100
sklearn.ensemble.forest.ExtraTreesClassifier(10)_n_jobsnull
sklearn.ensemble.forest.ExtraTreesClassifier(10)_oob_scorefalse
sklearn.ensemble.forest.ExtraTreesClassifier(10)_random_state3562
sklearn.ensemble.forest.ExtraTreesClassifier(10)_verbose0
sklearn.ensemble.forest.ExtraTreesClassifier(10)_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.

17 Evaluation measures

Area under ROC curve
0.9977 ± 0.0016
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.964 ± 0.0137
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.96 ± 0.0152
Cross-validation details (10-fold Crossvalidation)
KB relative information score
1819.0266 ± 1.5321
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0288 ± 0.0022
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.18
Cross-validation details (10-fold Crossvalidation)
Number of instances
2000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9642 ± 0.0129
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.964 ± 0.0137
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.3219
Cross-validation details (10-fold Crossvalidation)
Recall
0.964 ± 0.0137
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.16 ± 0.0122
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0925 ± 0.0055
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3083 ± 0.0182
Cross-validation details (10-fold Crossvalidation)