Run
10454814

Run 10454814

Task 3735 (Supervised Classification) pollen Uploaded 19-05-2020 by Marc Zöller
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  • automl_meta_features openml-python Sklearn_0.22.1.
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Flow

sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.on e_hot_encoding.OneHotEncoderComponent,step_1=sklearn.discriminant_analysis. LinearDiscriminantAnalysis)(1)Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using ``memory`` argument. The purpose of the pipeline is to assemble several steps that can be cross-validated together while setting different parameters. For this, it enables setting parameters of the various steps using their names and the parameter name separated by a '__', as in the example below. A step's estimator may be replaced entirely by setting the parameter with its name to another estimator, or a transformer removed by setting it to 'passthrough' or ``None``.
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_n_components73
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priorsnull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkage0.9592305740487875
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver"eigen"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariancefalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol0.032766101258234086
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}]
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_verbosefalse

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.

18 Evaluation measures

Area under ROC curve
0.4859 ± 0.0214
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.4924 ± 0.0181
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
-0.0151 ± 0.0367
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.0009 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.5003 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.5
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.4925 ± 0.0183
Cross-validation details (10-fold Crossvalidation)
Number of instances
3848
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.4925 ± 0.0184
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.4925 ± 0.0183
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.0007 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.5
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.5005 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.001 ± 0.001
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.4925 ± 0.0184
Cross-validation details (10-fold Crossvalidation)