Run
10437811

Run 10437811

Task 2274 (Supervised Classification) meta_ensembles.arff Uploaded 03-03-2020 by Fares Gaaloul
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(model=sklearn.ensemble.forest.RandomForestClassif ier)(2)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 to None.
sklearn.pipeline.Pipeline(model=sklearn.ensemble.forest.RandomForestClassifier)(2)_memorynull
sklearn.pipeline.Pipeline(model=sklearn.ensemble.forest.RandomForestClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "model", "step_name": "model"}}]
sklearn.ensemble.forest.RandomForestClassifier(61)_bootstraptrue
sklearn.ensemble.forest.RandomForestClassifier(61)_class_weightnull
sklearn.ensemble.forest.RandomForestClassifier(61)_criterion"gini"
sklearn.ensemble.forest.RandomForestClassifier(61)_max_depthnull
sklearn.ensemble.forest.RandomForestClassifier(61)_max_features0.49342778081534433
sklearn.ensemble.forest.RandomForestClassifier(61)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(61)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestClassifier(61)_min_impurity_split1e-07
sklearn.ensemble.forest.RandomForestClassifier(61)_min_samples_leaf2
sklearn.ensemble.forest.RandomForestClassifier(61)_min_samples_split15
sklearn.ensemble.forest.RandomForestClassifier(61)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(61)_n_estimators100
sklearn.ensemble.forest.RandomForestClassifier(61)_n_jobsnull
sklearn.ensemble.forest.RandomForestClassifier(61)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(61)_random_state24995
sklearn.ensemble.forest.RandomForestClassifier(61)_verbose0
sklearn.ensemble.forest.RandomForestClassifier(61)_warm_startfalse

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.

18 Evaluation measures

Area under ROC curve
0.729 ± 0.1761
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6137
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2912 ± 0.2348
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.2585 ± 0.1197
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2438 ± 0.0277
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.2904 ± 0.025
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.6622 ± 0.1159
Cross-validation details (10-fold Crossvalidation)
Number of instances
74
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.6205
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6622 ± 0.1159
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.5452 ± 0.2578
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8398 ± 0.0777
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.3782 ± 0.0332
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3489 ± 0.049
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9225 ± 0.1177
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.4099 ± 0.163
Cross-validation details (10-fold Crossvalidation)