Run
10453353

Run 10453353

Task 9946 (Supervised Classification) wdbc Uploaded 18-05-2020 by Marc Zöller
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • automl_meta_features openml-python Sklearn_0.22.1.
Issue #Downvotes for this reason By


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_alpha0.6870185049770876
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"entropy"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth14
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features4
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes26
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.7171109354729054
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.6978780371236817
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.08171640850086503
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.3501042451284326
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.3306168757717276
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators168
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
0.7298 ± 0.1978
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0022 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.4669 ± 0.0017
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
0.9984 ± 0.0021
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
0.4827 ± 0.002
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9984 ± 0.0021
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5
Cross-validation details (10-fold Crossvalidation)