Run
10560151

Run 10560151

Task 3913 (Supervised Classification) kc2 Uploaded 13-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(gradientboostingclassifier=sklearn.ensemble.gradi ent_boosting.GradientBoostingClassifier)(4)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 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 to None.
sklearn.pipeline.Pipeline(gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(4)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "gradientboostingclassifier", "step_name": "gradientboostingclassifier"}}]
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_criterion"friedman_mse"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_initnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_learning_rate0.05
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_loss"deviance"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_max_depth6
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_max_featuresnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_max_leaf_nodesnull
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_min_impurity_split1e-07
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_min_samples_leaf1
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_min_samples_split2
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_min_weight_fraction_leaf0.0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_n_estimators50
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_presort"auto"
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_random_state37073
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_subsample0.5
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_verbose0
sklearn.ensemble.gradient_boosting.GradientBoostingClassifier(26)_warm_startfalse

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
0.7948 ± 0.1078
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.8204 ± 0.0445
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.418 ± 0.1357
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.27 ± 0.1269
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2215 ± 0.0299
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3266 ± 0.0052
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.8333 ± 0.0416
Cross-validation details (10-fold Crossvalidation)
Number of instances
522
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.8182 ± 0.0491
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.8333 ± 0.0416
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.7318 ± 0.0173
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6784 ± 0.0886
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4037 ± 0.0065
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3516 ± 0.0409
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.871 ± 0.0956
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6836 ± 0.0605
Cross-validation details (10-fold Crossvalidation)