Run
10437806

Run 10437806

Task 2382 (Supervised Classification) wine Uploaded 03-03-2020 by Fares Gaaloul
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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
0.9992 ± 0.0071
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9775 ± 0.0295
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.9659 ± 0.0451
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.8727 ± 0.0455
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0766 ± 0.024
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.439 ± 0.0013
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9775 ± 0.0296
Cross-validation details (10-fold Crossvalidation)
Number of instances
178
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9778 ± 0.0252
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9775 ± 0.0296
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.5668 ± 0.0084
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.1745 ± 0.0549
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4684 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1417 ± 0.0399
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.3024 ± 0.0857
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.9803 ± 0.0251
Cross-validation details (10-fold Crossvalidation)