Run
10592296

Run 10592296

Task 167104 (Supervised Classification) Australian Uploaded 22-03-2023 by Yinuo Guo
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(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(42)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(42)_copytrue
sklearn.impute._base.SimpleImputer(42)_fill_valuenull
sklearn.impute._base.SimpleImputer(42)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(42)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(42)_strategy"mean"
sklearn.impute._base.SimpleImputer(42)_verbose"deprecated"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(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.ensemble._weight_boosting.AdaBoostClassifier)(1)_verbosefalse
sklearn.ensemble._weight_boosting.AdaBoostClassifier(6)_algorithm"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(6)_base_estimator"deprecated"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(6)_estimatornull
sklearn.ensemble._weight_boosting.AdaBoostClassifier(6)_learning_rate1.0
sklearn.ensemble._weight_boosting.AdaBoostClassifier(6)_n_estimators50
sklearn.ensemble._weight_boosting.AdaBoostClassifier(6)_random_state9197

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
0.8816
Per class
Cross-validation details (33% Holdout set)
F measure
0.8154
Per class
Cross-validation details (33% Holdout set)
Kappa
0.623
Cross-validation details (33% Holdout set)
KB relative information score
0.0457
Cross-validation details (33% Holdout set)
Mean absolute error
0.4739
Cross-validation details (33% Holdout set)
Mean prior absolute error
0.4915
Cross-validation details (33% Holdout set)
Weighted recall
0.815
Cross-validation details (33% Holdout set)
Number of instances
227
Per class
Cross-validation details (33% Holdout set)
Precision
0.8164
Per class
Cross-validation details (33% Holdout set)
Predictive accuracy
0.815
Cross-validation details (33% Holdout set)
Prior entropy
0.9842
Cross-validation details (33% Holdout set)
Relative absolute error
0.9641
Cross-validation details (33% Holdout set)
Root mean prior squared error
0.4945
Cross-validation details (33% Holdout set)
Root mean squared error
0.4767
Cross-validation details (33% Holdout set)
Root relative squared error
0.9639
Cross-validation details (33% Holdout set)
Unweighted recall
0.8133
Cross-validation details (33% Holdout set)