Run
10560529

Run 10560529

Task 9952 (Supervised Classification) phoneme Uploaded 14-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(nystroem=sklearn.kernel_approximation.Nystroem,fe atureagglomeration=sklearn.cluster.hierarchical.FeatureAgglomeration,gaussi annb=sklearn.naive_bayes.GaussianNB)(2)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 to None.
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_affinity"euclidean"
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_compute_full_tree"auto"
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_connectivitynull
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_linkage"average"
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_memorynull
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_n_clusters2
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_pooling_func{"oml-python:serialized_object": "function", "value": "numpy.mean"}
sklearn.pipeline.Pipeline(nystroem=sklearn.kernel_approximation.Nystroem,featureagglomeration=sklearn.cluster.hierarchical.FeatureAgglomeration,gaussiannb=sklearn.naive_bayes.GaussianNB)(2)_memorynull
sklearn.pipeline.Pipeline(nystroem=sklearn.kernel_approximation.Nystroem,featureagglomeration=sklearn.cluster.hierarchical.FeatureAgglomeration,gaussiannb=sklearn.naive_bayes.GaussianNB)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "nystroem", "step_name": "nystroem"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "featureagglomeration", "step_name": "featureagglomeration"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "gaussiannb", "step_name": "gaussiannb"}}]
sklearn.kernel_approximation.Nystroem(4)_coef0null
sklearn.kernel_approximation.Nystroem(4)_degreenull
sklearn.kernel_approximation.Nystroem(4)_gamma0.15000000000000002
sklearn.kernel_approximation.Nystroem(4)_kernel"laplacian"
sklearn.kernel_approximation.Nystroem(4)_kernel_paramsnull
sklearn.kernel_approximation.Nystroem(4)_n_components9
sklearn.kernel_approximation.Nystroem(4)_random_state56958
sklearn.naive_bayes.GaussianNB(21)_priorsnull

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
0.695 ± 0.0742
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6867 ± 0.042
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2096 ± 0.1604
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.123 ± 0.1224
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3584 ± 0.0478
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4147 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7302 ± 0.0232
Cross-validation details (10-fold Crossvalidation)
Number of instances
5404
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7045 ± 0.0315
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7302 ± 0.0232
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.8732 ± 0.001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8642 ± 0.1156
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4554 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4392 ± 0.0107
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9644 ± 0.024
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5862 ± 0.0755
Cross-validation details (10-fold Crossvalidation)