Run
10464770

Run 10464770

Task 9983 (Supervised Classification) eeg-eye-state Uploaded 22-05-2020 by Marc Zöller
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  • automl_meta_features openml-python Sklearn_0.22.1.
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Flow

sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBin sDiscretizer,step_1=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.ensemble._weight_boosting.AdaBoostClassifier(2)_algorithm"SAMME"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_base_estimatornull
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_learning_rate0.003906185811304006
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators331
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_random_state42
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBinsDiscretizer,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBinsDiscretizer,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._discretization.KBinsDiscretizer,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_verbosefalse
sklearn.preprocessing._discretization.KBinsDiscretizer(1)_encode"ordinal"
sklearn.preprocessing._discretization.KBinsDiscretizer(1)_n_bins82
sklearn.preprocessing._discretization.KBinsDiscretizer(1)_strategy"kmeans"

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
0.6556 ± 0.0163
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.494 ± 0.0157
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.0818 ± 0.0237
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0693 ± 0.009
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.466 ± 0.0036
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5788 ± 0.0103
Cross-validation details (10-fold Crossvalidation)
Number of instances
14980
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.6035 ± 0.0233
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5788 ± 0.0103
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9924 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9418 ± 0.0073
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4855 ± 0.002
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9762 ± 0.0039
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5379 ± 0.0111
Cross-validation details (10-fold Crossvalidation)