Run
10453584

Run 10453584

Task 146821 (Supervised Classification) car Uploaded 18-05-2020 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=automl.components.feature_preprocessing.on e_hot_encoding.OneHotEncoderComponent,step_1=sklearn.ensemble._weight_boost ing.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.R"
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_base_estimatornull
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_learning_rate4.434914551779938
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_n_estimators612
sklearn.ensemble._weight_boosting.AdaBoostClassifier(2)_random_state42
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,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=automl.components.feature_preprocessing.one_hot_encoding.OneHotEncoderComponent,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)(1)_verbosefalse

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
0.828 ± 0.0457
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7262 ± 0.0687
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4046 ± 0.2208
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.3604 ± 0.1879
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3301 ± 0.0391
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.229 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7228 ± 0.0716
Cross-validation details (10-fold Crossvalidation)
Number of instances
1728
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.733 ± 0.0371
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7228 ± 0.0716
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.2058 ± 0.0088
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.4417 ± 0.1701
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3381 ± 0.0008
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.39 ± 0.0365
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1.1534 ± 0.1076
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5647 ± 0.2027
Cross-validation details (10-fold Crossvalidation)