Run
10592445

Run 10592445

Task 11 (Supervised Classification) balance-scale Uploaded 23-03-2023 by Takeaki Sakabe
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.linear_model._ridge.RidgeClassifier)(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(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.linear_model._ridge.RidgeClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._ridge.RidgeClassifier)(1)_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.linear_model._ridge.RidgeClassifier)(1)_verbosefalse
sklearn.linear_model._ridge.RidgeClassifier(2)_alpha1.0
sklearn.linear_model._ridge.RidgeClassifier(2)_class_weightnull
sklearn.linear_model._ridge.RidgeClassifier(2)_copy_Xtrue
sklearn.linear_model._ridge.RidgeClassifier(2)_fit_intercepttrue
sklearn.linear_model._ridge.RidgeClassifier(2)_max_iternull
sklearn.linear_model._ridge.RidgeClassifier(2)_positivefalse
sklearn.linear_model._ridge.RidgeClassifier(2)_random_state27302
sklearn.linear_model._ridge.RidgeClassifier(2)_solver"auto"
sklearn.linear_model._ridge.RidgeClassifier(2)_tol0.0001

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.

16 Evaluation measures

0.8739 ± 0.0204
Per class
Cross-validation details (10-fold Crossvalidation)
0.7478 ± 0.0412
Cross-validation details (10-fold Crossvalidation)
0.6879 ± 0.037
Cross-validation details (10-fold Crossvalidation)
0.0907 ± 0.0151
Cross-validation details (10-fold Crossvalidation)
0.3798 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
0.864 ± 0.0226
Cross-validation details (10-fold Crossvalidation)
625
Per class
Cross-validation details (10-fold Crossvalidation)
0.864 ± 0.0226
Cross-validation details (10-fold Crossvalidation)
1.3181 ± 0.0124
Cross-validation details (10-fold Crossvalidation)
0.2387 ± 0.0393
Cross-validation details (10-fold Crossvalidation)
0.4356 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
0.3011 ± 0.0257
Cross-validation details (10-fold Crossvalidation)
0.6913 ± 0.0577
Cross-validation details (10-fold Crossvalidation)
0.625 ± 0.0145
Cross-validation details (10-fold Crossvalidation)