Run
8

Run 8

Task 11 (Supervised Classification) kr-vs-kp Uploaded 29-10-2019 by Continuous Integration
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  • openml-python Sklearn_0.21.2. study_6 study_17 study_35 study_52 study_81 study_84 study_3170 study_3608 study_3994 study_3996 study_3999 study_4418 study_5311 study_8310 study_11997 study_12006 study_13178 study_17523 study_17528
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Flow

sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,Varian ceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold, Estimator=sklearn.naive_bayes.GaussianNB)(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.impute._base.SimpleImputer(1)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(1)_copytrue
sklearn.impute._base.SimpleImputer(1)_fill_valuenull
sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(1)_strategy"median"
sklearn.impute._base.SimpleImputer(1)_verbose0
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.naive_bayes.GaussianNB)(1)_memorynull
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.naive_bayes.GaussianNB)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "VarianceThreshold", "step_name": "VarianceThreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "Estimator", "step_name": "Estimator"}}]
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,VarianceThreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,Estimator=sklearn.naive_bayes.GaussianNB)(1)_verbosefalse
sklearn.feature_selection.variance_threshold.VarianceThreshold(1)_threshold0.05
sklearn.naive_bayes.GaussianNB(1)_priorsnull
sklearn.naive_bayes.GaussianNB(1)_var_smoothing1e-09

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

0.9342
Per class
Cross-validation details (10% Holdout set)
0.6217
Per class
Cross-validation details (10% Holdout set)
0.3416
Cross-validation details (10% Holdout set)
0.3211
Cross-validation details (10% Holdout set)
0.3367
Cross-validation details (10% Holdout set)
0.4984
Cross-validation details (10% Holdout set)
0.6565
Cross-validation details (10% Holdout set)
1054
Per class
Cross-validation details (10% Holdout set)
0.8025
Per class
Cross-validation details (10% Holdout set)
0.6565
Cross-validation details (10% Holdout set)
0.9969
Cross-validation details (10% Holdout set)
0.6755
Cross-validation details (10% Holdout set)
0.4989
Cross-validation details (10% Holdout set)
0.5746
Cross-validation details (10% Holdout set)
1.1517
Cross-validation details (10% Holdout set)
0.6791
Cross-validation details (10% Holdout set)