Run
10453888

Run 10453888

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_alpha0.5048379235325333
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_features26
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes2098
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.8057035395253386
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.5211844345360797
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.17179760078334622
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.47860686546868103
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.3165615008055636
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators362
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
0.8981 ± 0.006
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2792 ± 0.0108
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2937 ± 0.0024
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.1419 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1679 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.4403 ± 0.0087
Cross-validation details (10-fold Crossvalidation)
Number of instances
2126
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.4403 ± 0.0087
Cross-validation details (10-fold Crossvalidation)
Prior entropy
2.9134 ± 0.0053
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.845 ± 0.0027
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
0.2591 ± 0.001
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8942 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.1944 ± 0.0036
Cross-validation details (10-fold Crossvalidation)