Run
9855758

Run 9855758

Task 9955 (Supervised Classification) one-hundred-plants-shape Uploaded 23-10-2018 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._encoders.OneHotEncoder(3)_n_valuesnull
sklearn.preprocessing._encoders.OneHotEncoder(3)_sparsetrue
sklearn.svm.classes.SVC(22)_C164.53414321670832
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sklearn.svm.classes.SVC(22)_coef00.0
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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
sklearn.feature_selection.variance_threshold.VarianceThreshold(18)_threshold0.0
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,svc=sklearn.svm.classes.SVC)(1)_memorynull

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
0.7828 ± 0.0236
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6151
Per class
Kappa
0.5657 ± 0.0471
Cross-validation details (10-fold Crossvalidation)
KB relative information score
910.4985 ± 7.4967
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0086 ± 0.0009
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)
Precision
0.716
Per class
Predictive accuracy
0.57 ± 0.0468
Cross-validation details (10-fold Crossvalidation)
Prior entropy
6.6439
Cross-validation details (10-fold Crossvalidation)
Recall
0.57 ± 0.0468
Per class
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4343 ± 0.0472
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.0995
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.0927 ± 0.0051
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.932 ± 0.0513
Cross-validation details (10-fold Crossvalidation)