Run
10453832

Run 10453832

Task 9979 (Supervised Classification) cardiotocography 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=sklearn.ensemble._forest.RandomForestClass ifier)(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._forest.RandomForestClassifier(2)_bootstraptrue
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.4392862824105862
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"entropy"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth10
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features7
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes597
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.8365717851360782
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.698243536717697
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.07267950314556969
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.12637372882288544
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.25685748341771886
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators34
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobs1
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_scoretrue
sklearn.ensemble._forest.RandomForestClassifier(2)_random_state42
sklearn.ensemble._forest.RandomForestClassifier(2)_verbose0
sklearn.ensemble._forest.RandomForestClassifier(2)_warm_startfalse
sklearn.pipeline.Pipeline(step_0=sklearn.ensemble._forest.RandomForestClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(step_0=sklearn.ensemble._forest.RandomForestClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}]
sklearn.pipeline.Pipeline(step_0=sklearn.ensemble._forest.RandomForestClassifier)(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.

16 Evaluation measures

Area under ROC curve
0.9255 ± 0.0051
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2882 ± 0.0075
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.1707 ± 0.0073
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1524 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1679 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.4478 ± 0.0058
Cross-validation details (10-fold Crossvalidation)
Number of instances
2126
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.4478 ± 0.0058
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.9134 ± 0.0053
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.9078 ± 0.0043
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2897 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2656 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9167 ± 0.0042
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.1971 ± 0.0029
Cross-validation details (10-fold Crossvalidation)