Run
10453410

Run 10453410

Task 9946 (Supervised Classification) wdbc 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_alpha0.7193945341805551
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"entropy"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth3
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features20
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes154
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.20161773064472407
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.35051259595653517
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.39968526189951126
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.4336505792964151
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.05712254565215863
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators65
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
0.4952
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.0025 ± 0.0007
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4683 ± 0.0018
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4676 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.6274 ± 0.0073
Cross-validation details (10-fold Crossvalidation)
Number of instances
569
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6274 ± 0.0073
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9526 ± 0.0055
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.0013 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4835 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4835 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5
Cross-validation details (10-fold Crossvalidation)