Run
10435698

Run 10435698

Task 31 (Supervised Classification) credit-g Uploaded 10-01-2020 by Yaron Geffen
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Flow

sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler ,classifier=sklearn.neighbors._classification.KNeighborsClassifier)(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.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.neighbors._classification.KNeighborsClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.neighbors._classification.KNeighborsClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.neighbors._classification.KNeighborsClassifier)(1)_verbosefalse
sklearn.preprocessing._data.StandardScaler(1)_copytrue
sklearn.preprocessing._data.StandardScaler(1)_with_meantrue
sklearn.preprocessing._data.StandardScaler(1)_with_stdtrue
sklearn.neighbors._classification.KNeighborsClassifier(2)_algorithm"auto"
sklearn.neighbors._classification.KNeighborsClassifier(2)_leaf_size30
sklearn.neighbors._classification.KNeighborsClassifier(2)_metric"minkowski"
sklearn.neighbors._classification.KNeighborsClassifier(2)_metric_paramsnull
sklearn.neighbors._classification.KNeighborsClassifier(2)_n_jobsnull
sklearn.neighbors._classification.KNeighborsClassifier(2)_n_neighbors10
sklearn.neighbors._classification.KNeighborsClassifier(2)_p2
sklearn.neighbors._classification.KNeighborsClassifier(2)_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

0.7545 ± 0.0454
Per class
Cross-validation details (10-fold Crossvalidation)
0.7015 ± 0.0436
Per class
Cross-validation details (10-fold Crossvalidation)
0.2589 ± 0.1082
Cross-validation details (10-fold Crossvalidation)
0.209 ± 0.0554
Cross-validation details (10-fold Crossvalidation)
0.325 ± 0.0179
Cross-validation details (10-fold Crossvalidation)
0.4202
Cross-validation details (10-fold Crossvalidation)
0.743 ± 0.0353
Cross-validation details (10-fold Crossvalidation)
1000
Per class
Cross-validation details (10-fold Crossvalidation)
0.7307 ± 0.0608
Per class
Cross-validation details (10-fold Crossvalidation)
0.743 ± 0.0353
Cross-validation details (10-fold Crossvalidation)
0.8813
Cross-validation details (10-fold Crossvalidation)
0.7735 ± 0.0427
Cross-validation details (10-fold Crossvalidation)
0.4583
Cross-validation details (10-fold Crossvalidation)
0.4182 ± 0.0191
Cross-validation details (10-fold Crossvalidation)
0.9126 ± 0.0418
Cross-validation details (10-fold Crossvalidation)
0.6069 ± 0.0475
Cross-validation details (10-fold Crossvalidation)