Run
10560505

Run 10560505

Task 11 (Supervised Classification) balance-scale Uploaded 14-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(featureagglomeration=sklearn.cluster.hierarchical .FeatureAgglomeration,kneighborsclassifier=sklearn.neighbors.classification .KNeighborsClassifier)(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(featureagglomeration=sklearn.cluster.hierarchical.FeatureAgglomeration,kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier)(2)_memorynull
sklearn.pipeline.Pipeline(featureagglomeration=sklearn.cluster.hierarchical.FeatureAgglomeration,kneighborsclassifier=sklearn.neighbors.classification.KNeighborsClassifier)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "featureagglomeration", "step_name": "featureagglomeration"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "kneighborsclassifier", "step_name": "kneighborsclassifier"}}]
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_affinity"euclidean"
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_compute_full_tree"auto"
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_connectivitynull
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_linkage"average"
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_memorynull
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_n_clusters2
sklearn.cluster.hierarchical.FeatureAgglomeration(5)_pooling_func{"oml-python:serialized_object": "function", "value": "numpy.mean"}
sklearn.neighbors.classification.KNeighborsClassifier(45)_algorithm"auto"
sklearn.neighbors.classification.KNeighborsClassifier(45)_leaf_size30
sklearn.neighbors.classification.KNeighborsClassifier(45)_metric"minkowski"
sklearn.neighbors.classification.KNeighborsClassifier(45)_metric_paramsnull
sklearn.neighbors.classification.KNeighborsClassifier(45)_n_jobs1
sklearn.neighbors.classification.KNeighborsClassifier(45)_n_neighbors5
sklearn.neighbors.classification.KNeighborsClassifier(45)_p2
sklearn.neighbors.classification.KNeighborsClassifier(45)_weights"uniform"

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
0.7489 ± 0.218
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.6042 ± 0.228
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.3034 ± 0.3987
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.3184 ± 0.3403
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.2743 ± 0.134
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.3798 ± 0.0012
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.6112 ± 0.2206
Cross-validation details (10-fold Crossvalidation)
Number of instances
625
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.6056 ± 0.2323
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.6112 ± 0.2206
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.3181 ± 0.0124
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7224 ± 0.3535
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4356 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4053 ± 0.1238
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9305 ± 0.2851
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5042 ± 0.2417
Cross-validation details (10-fold Crossvalidation)