Run
10435266

Run 10435266

Task 37 (Supervised Classification) diabetes Uploaded 30-12-2019 by you xiao
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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_depth17
sklearn.ensemble.forest.RandomForestClassifier(61)_max_features0.4145142745799847
sklearn.ensemble.forest.RandomForestClassifier(61)_max_leaf_nodesnull
sklearn.ensemble.forest.RandomForestClassifier(61)_min_impurity_decrease0.0
sklearn.ensemble.forest.RandomForestClassifier(61)_min_impurity_splitnull
sklearn.ensemble.forest.RandomForestClassifier(61)_min_samples_leaf2
sklearn.ensemble.forest.RandomForestClassifier(61)_min_samples_split9
sklearn.ensemble.forest.RandomForestClassifier(61)_min_weight_fraction_leaf0.0
sklearn.ensemble.forest.RandomForestClassifier(61)_n_estimators100
sklearn.ensemble.forest.RandomForestClassifier(61)_n_jobs-1
sklearn.ensemble.forest.RandomForestClassifier(61)_oob_scorefalse
sklearn.ensemble.forest.RandomForestClassifier(61)_random_state1
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
0.8308 ± 0.047
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7662 ± 0.0419
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4797 ± 0.0944
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.3194 ± 0.0668
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3149 ± 0.0251
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4545 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7695 ± 0.0414
Cross-validation details (10-fold Crossvalidation)
Number of instances
768
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7652 ± 0.0435
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7695 ± 0.0414
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.9331 ± 0.0032
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.6928 ± 0.0554
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4766 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.3972 ± 0.029
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8333 ± 0.0614
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7338 ± 0.0466
Cross-validation details (10-fold Crossvalidation)