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10592338

Run 10592338

Task 167201 (Supervised Classification) connect-4 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_indicator	false
sklearn.impute._base.SimpleImputer(42)_copy	true
sklearn.impute._base.SimpleImputer(42)_fill_value	null
sklearn.impute._base.SimpleImputer(42)_keep_empty_features	false
sklearn.impute._base.SimpleImputer(42)_missing_values	NaN
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)_memory	null
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)_verbose	false
sklearn.ensemble._weight_boosting.AdaBoostClassifier(6)_algorithm	"SAMME.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(6)_base_estimator	"deprecated"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(6)_estimator	null
sklearn.ensemble._weight_boosting.AdaBoostClassifier(6)_learning_rate	1.0
sklearn.ensemble._weight_boosting.AdaBoostClassifier(6)_n_estimators	50
sklearn.ensemble._weight_boosting.AdaBoostClassifier(6)_random_state	9197

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

Area under ROC curve

0.8121

Per class

Cross-validation details (33% Holdout set)

0.3212

Cross-validation details (33% Holdout set)

KB relative information score

-0.2796

Cross-validation details (33% Holdout set)

Mean absolute error

0.4403

Cross-validation details (33% Holdout set)

Mean prior absolute error

0.3315

Cross-validation details (33% Holdout set)

Weighted recall

0.7274

Cross-validation details (33% Holdout set)

Number of instances

22293

Per class

Cross-validation details (33% Holdout set)

Predictive accuracy

0.7274

Cross-validation details (33% Holdout set)

Prior entropy

1.2189

Cross-validation details (33% Holdout set)

Relative absolute error

1.3282

Cross-validation details (33% Holdout set)

Root mean prior squared error

0.4073

Cross-validation details (33% Holdout set)

Root mean squared error

0.4671

Cross-validation details (33% Holdout set)

Root relative squared error

1.1468

Cross-validation details (33% Holdout set)

Unweighted recall

0.4534

Cross-validation details (33% Holdout set)