Run
6188

Run 6188

Task 1196 (Supervised Classification) iris Uploaded 16-01-2024 by Continuous Integration
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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`. For an example use case of `Pipeline` combined with :class:`~sklearn.model_selection.GridSearchCV`, refer to :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The example :ref:`sphx_glr_auto_exampl...
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)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(1)_strategy"most_frequent"
sklearn.feature_selection._variance_threshold.VarianceThreshold(1)_threshold0.05
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.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

0.9944 ± 0.0064
Per class
0.96 ± 0.048
Per class
0.94 ± 0.0699
0.93 ± 0.0568
0.0365 ± 0.027
0.4444
0.96 ± 0.0466
150
Per class
0.96 ± 0.0355
Per class
0.96 ± 0.0466
1.585
0.0822 ± 0.0608
0.4714
0.1538 ± 0.0914
0.3262 ± 0.1939
0.96 ± 0.0466