Run
10454079

Run 10454079

Task 53 (Supervised Classification) vehicle 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_alpha0.863905435007041
sklearn.ensemble._forest.RandomForestClassifier(2)_class_weightnull
sklearn.ensemble._forest.RandomForestClassifier(2)_criterion"gini"
sklearn.ensemble._forest.RandomForestClassifier(2)_max_depth3
sklearn.ensemble._forest.RandomForestClassifier(2)_max_features3
sklearn.ensemble._forest.RandomForestClassifier(2)_max_leaf_nodes575
sklearn.ensemble._forest.RandomForestClassifier(2)_max_samples0.10982829195911556
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_decrease0.6368305323675282
sklearn.ensemble._forest.RandomForestClassifier(2)_min_impurity_splitnull
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_leaf0.1922399723108282
sklearn.ensemble._forest.RandomForestClassifier(2)_min_samples_split0.47854885996434743
sklearn.ensemble._forest.RandomForestClassifier(2)_min_weight_fraction_leaf0.3060539691164381
sklearn.ensemble._forest.RandomForestClassifier(2)_n_estimators522
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
0.4953
Per class
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.0018 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3749 ± 0
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3748 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.2565 ± 0.0046
Cross-validation details (10-fold Crossvalidation)
Number of instances
846
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.2565 ± 0.0046
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.9991 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
1.0001 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4329 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4329 ± 0
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
1 ± 0
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.25
Cross-validation details (10-fold Crossvalidation)