Run
10592564

Run 10592564

Task 16 (Supervised Classification) mfeat-karhunen Uploaded 23-03-2023 by Takeaki Sakabe
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(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.ensemble._weight_boosting.AdaBoostClassifier)(2)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(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.ensemble._weight_boosting.AdaBoostClassifier)(2)_verbosefalse
sklearn.ensemble._weight_boosting.AdaBoostClassifier(7)_algorithm"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(7)_base_estimator"deprecated"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(7)_estimatornull
sklearn.ensemble._weight_boosting.AdaBoostClassifier(7)_learning_rate1.0
sklearn.ensemble._weight_boosting.AdaBoostClassifier(7)_n_estimators50
sklearn.ensemble._weight_boosting.AdaBoostClassifier(7)_random_state3833

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
0.6919 ± 0.0387
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.2952
Per class
Kappa
0.2578 ± 0.068
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.1477 ± 0.0265
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1647 ± 0.0042
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.18
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.332 ± 0.0612
Cross-validation details (10-fold Crossvalidation)
Number of instances
2000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.3184
Per class
Predictive accuracy
0.332 ± 0.0612
Cross-validation details (10-fold Crossvalidation)
Prior entropy
3.3219
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.915 ± 0.0234
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2896 ± 0.0035
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9654 ± 0.0117
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.332 ± 0.0612
Cross-validation details (10-fold Crossvalidation)