Run
10453850

Run 10453850

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)_bootstrapfalse
sklearn.ensemble._forest.RandomForestClassifier(2)_ccp_alpha0.05155016441180105
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"entropy"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth33
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features28
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes796
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.7389932915496333
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.4362814989938558
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.13099821032485282
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.08196191047753272
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.27024172912792754
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators393
sklearn.ensemble._forest.RandomForestClassifier(2)_n_jobs1
sklearn.ensemble._forest.RandomForestClassifier(2)_oob_scorefalse
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
0.9316 ± 0.0029
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4984 ± 0.0111
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.4742 ± 0.0042
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1077 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1679 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5969 ± 0.0088
Cross-validation details (10-fold Crossvalidation)
Number of instances
2126
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5969 ± 0.0088
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.9134 ± 0.0053
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6413 ± 0.0026
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
0.2225 ± 0.0009
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.7678 ± 0.0028
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.2928 ± 0.0052
Cross-validation details (10-fold Crossvalidation)