Run
10457876

Run 10457876

Task 9900 (Supervised Classification) abalone 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.cluster._agglomerative .FeatureAgglomeration,step_2=sklearn.discriminant_analysis.LinearDiscrimina ntAnalysis)(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_components24
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)_tol0.052398784534624605
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_affinity"cosine"
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_compute_full_treefalse
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_connectivitynull
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_distance_thresholdnull
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_linkage"single"
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_memorynull
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_n_clusters2
sklearn.cluster._agglomerative.FeatureAgglomeration(1)_pooling_func{"oml-python:serialized_object": "function", "value": "numpy.amax"}
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.cluster._agglomerative.FeatureAgglomeration,step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.cluster._agglomerative.FeatureAgglomeration,step_2=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"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.cluster._agglomerative.FeatureAgglomeration,step_2=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
0.7639 ± 0.0173
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5276 ± 0.0221
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3336 ± 0.0322
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.269 ± 0.0128
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3586 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4441 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5583 ± 0.0215
Cross-validation details (10-fold Crossvalidation)
Number of instances
4177
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5306 ± 0.0289
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5583 ± 0.0215
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.584 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8073 ± 0.0116
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4712 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4202 ± 0.0053
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8916 ± 0.0113
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.551 ± 0.021
Cross-validation details (10-fold Crossvalidation)