Run
10593672

Run 10593672

Task 361444 (Supervised Classification) phoneme Uploaded 09-05-2023 by Takeaki Sakabe
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.neighbors._classification.KNeighborsClassifier)(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 it to `'passthrough'` or `None`.
sklearn.impute._base.SimpleImputer(43)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(43)_copytrue
sklearn.impute._base.SimpleImputer(43)_fill_valuenull
sklearn.impute._base.SimpleImputer(43)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(43)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(43)_strategy"mean"
sklearn.impute._base.SimpleImputer(43)_verbose"deprecated"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neighbors._classification.KNeighborsClassifier)(2)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neighbors._classification.KNeighborsClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.neighbors._classification.KNeighborsClassifier)(2)_verbosefalse
sklearn.neighbors._classification.KNeighborsClassifier(19)_algorithm"auto"
sklearn.neighbors._classification.KNeighborsClassifier(19)_leaf_size30
sklearn.neighbors._classification.KNeighborsClassifier(19)_metric"minkowski"
sklearn.neighbors._classification.KNeighborsClassifier(19)_metric_paramsnull
sklearn.neighbors._classification.KNeighborsClassifier(19)_n_jobsnull
sklearn.neighbors._classification.KNeighborsClassifier(19)_n_neighbors5
sklearn.neighbors._classification.KNeighborsClassifier(19)_p2
sklearn.neighbors._classification.KNeighborsClassifier(19)_weights"uniform"

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
0.913 ± 0.0047
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
F measure
0.857 ± 0.0054
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
Kappa
0.6517 ± 0.0135
Cross-validation details (5 times 2-fold Crossvalidation)
KB relative information score
0.5609 ± 0.009
Cross-validation details (5 times 2-fold Crossvalidation)
Mean absolute error
0.1763 ± 0.0033
Cross-validation details (5 times 2-fold Crossvalidation)
Mean prior absolute error
0.4147 ± 0
Cross-validation details (5 times 2-fold Crossvalidation)
Weighted recall
0.8587 ± 0.0052
Cross-validation details (5 times 2-fold Crossvalidation)
Number of instances
27020
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
Precision
0.8563 ± 0.0054
Per class
Cross-validation details (5 times 2-fold Crossvalidation)
Predictive accuracy
0.8587 ± 0.0052
Cross-validation details (5 times 2-fold Crossvalidation)
Prior entropy
0.8732 ± 0
Cross-validation details (5 times 2-fold Crossvalidation)
Relative absolute error
0.4251 ± 0.0081
Cross-validation details (5 times 2-fold Crossvalidation)
Root mean prior squared error
0.4554 ± 0
Cross-validation details (5 times 2-fold Crossvalidation)
Root mean squared error
0.317 ± 0.0036
Cross-validation details (5 times 2-fold Crossvalidation)
Root relative squared error
0.6961 ± 0.008
Cross-validation details (5 times 2-fold Crossvalidation)
Unweighted recall
0.8188 ± 0.0077
Cross-validation details (5 times 2-fold Crossvalidation)