Run
10592617

Run 10592617

Task 37 (Supervised Classification) diabetes Uploaded 23-03-2023 by Takeaki Sakabe
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


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_state2000
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.

18 Evaluation measures

Area under ROC curve
0.7267 ± 0.0611
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7679 ± 0.0513
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4791 ± 0.1183
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.487 ± 0.1082
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.224 ± 0.0469
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4545 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.776 ± 0.0469
Cross-validation details (10-fold Crossvalidation)
Number of instances
768
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7713 ± 0.0512
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.776 ± 0.0469
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9331 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.4928 ± 0.1037
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4766 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4732 ± 0.0485
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9929 ± 0.1029
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7267 ± 0.0611
Cross-validation details (10-fold Crossvalidation)