Run
10458569

Run 10458569

Task 9981 (Supervised Classification) cnae-9 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=sklearn.impute._knn.KNNImputer,step_1=skle arn.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_components78
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_priorsnull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_shrinkagenull
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_solver"svd"
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_store_covariancefalse
sklearn.discriminant_analysis.LinearDiscriminantAnalysis(4)_tol0.010275938098095835
sklearn.impute._knn.KNNImputer(1)_add_indicatorfalse
sklearn.impute._knn.KNNImputer(1)_copyfalse
sklearn.impute._knn.KNNImputer(1)_metric"nan_euclidean"
sklearn.impute._knn.KNNImputer(1)_missing_valuesNaN
sklearn.impute._knn.KNNImputer(1)_n_neighbors8
sklearn.impute._knn.KNNImputer(1)_weights"distance"
sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,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=sklearn.impute._knn.KNNImputer,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

0.9683 ± 0.0111
Per class
Cross-validation details (10-fold Crossvalidation)
0.8752 ± 0.0267
Per class
Cross-validation details (10-fold Crossvalidation)
0.8583 ± 0.0295
Cross-validation details (10-fold Crossvalidation)
0.8681 ± 0.0274
Cross-validation details (10-fold Crossvalidation)
0.0279 ± 0.0058
Cross-validation details (10-fold Crossvalidation)
0.1975
Cross-validation details (10-fold Crossvalidation)
0.8741 ± 0.0263
Cross-validation details (10-fold Crossvalidation)
1080
Per class
Cross-validation details (10-fold Crossvalidation)
0.8821 ± 0.0214
Per class
Cross-validation details (10-fold Crossvalidation)
0.8741 ± 0.0263
Cross-validation details (10-fold Crossvalidation)
3.1699
Cross-validation details (10-fold Crossvalidation)
0.1411 ± 0.0293
Cross-validation details (10-fold Crossvalidation)
0.3143
Cross-validation details (10-fold Crossvalidation)
0.1652 ± 0.0167
Cross-validation details (10-fold Crossvalidation)
0.5256 ± 0.0533
Cross-validation details (10-fold Crossvalidation)
0.8741 ± 0.0263
Cross-validation details (10-fold Crossvalidation)