Run
10089009

Run 10089009

Task 3954 (Supervised Classification) MagicTelescope Uploaded 25-01-2019 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(imputer=sklearn.pr eprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.St andardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.imput e.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder )),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceT hreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(1 )Automatically created scikit-learn flow.
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.tree.tree.DecisionTreeClassifier(29)_class_weightnull
sklearn.tree.tree.DecisionTreeClassifier(29)_criterion"gini"
sklearn.tree.tree.DecisionTreeClassifier(29)_max_depthnull
sklearn.tree.tree.DecisionTreeClassifier(29)_max_features1.0
sklearn.tree.tree.DecisionTreeClassifier(29)_max_leaf_nodesnull
sklearn.tree.tree.DecisionTreeClassifier(29)_min_impurity_decrease0.0
sklearn.tree.tree.DecisionTreeClassifier(29)_min_impurity_splitnull
sklearn.tree.tree.DecisionTreeClassifier(29)_min_samples_leaf15
sklearn.tree.tree.DecisionTreeClassifier(29)_min_samples_split7
sklearn.tree.tree.DecisionTreeClassifier(29)_min_weight_fraction_leaf0.0
sklearn.tree.tree.DecisionTreeClassifier(29)_presortfalse
sklearn.tree.tree.DecisionTreeClassifier(29)_random_state34672
sklearn.tree.tree.DecisionTreeClassifier(29)_splitter"best"
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(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(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(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(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(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(1)_memorynull
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(1)_memorynull
sklearn.feature_selection.variance_threshold.VarianceThreshold(18)_threshold0.0

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
0.8883 ± 0.0069
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8404 ± 0.003
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.6472 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
KB relative information score
10891.9249 ± 12.8022
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1947 ± 0.0029
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.456 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Number of instances
19020
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8402 ± 0.003
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.842 ± 0.003
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9355
Cross-validation details (10-fold Crossvalidation)
Recall
0.842 ± 0.003
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4269 ± 0.0063
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4775 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3457 ± 0.004
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.724 ± 0.0083
Cross-validation details (10-fold Crossvalidation)