Run
9722083

Run 9722083

Task 9955 (Supervised Classification) one-hundred-plants-shape Uploaded 21-10-2018 by Scikit-learn Bot
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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,svc=sklearn.svm.classes.SVC)(1)Automatically created scikit-learn flow.
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sklearn.preprocessing.imputation.Imputer(29)_copytrue
sklearn.preprocessing.imputation.Imputer(29)_missing_values"NaN"
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sklearn.preprocessing.data.StandardScaler(14)_with_stdtrue
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sklearn.preprocessing._encoders.OneHotEncoder(3)_sparsetrue
sklearn.svm.classes.SVC(22)_C0.23896880418608835
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sklearn.svm.classes.SVC(22)_gamma0.002621784979684833
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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
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sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)(1)_memorynull
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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
0.5723 ± 0.0084
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.1446 ± 0.0168
Cross-validation details (10-fold Crossvalidation)
KB relative information score
242.0428 ± 2.6829
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0169 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.0198
Cross-validation details (10-fold Crossvalidation)
Number of instances
1600
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.1531 ± 0.0167
Cross-validation details (10-fold Crossvalidation)
Prior entropy
6.6439
Cross-validation details (10-fold Crossvalidation)
Recall
0.1531 ± 0.0167
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8554 ± 0.0169
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.0995
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1301 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.308 ± 0.013
Cross-validation details (10-fold Crossvalidation)