Run
10435697

Run 10435697

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_neighbors5
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

0.7271 ± 0.039
Per class
Cross-validation details (10-fold Crossvalidation)
0.7296 ± 0.0364
Per class
Cross-validation details (10-fold Crossvalidation)
0.3308 ± 0.0907
Cross-validation details (10-fold Crossvalidation)
0.2234 ± 0.0422
Cross-validation details (10-fold Crossvalidation)
0.3152 ± 0.0145
Cross-validation details (10-fold Crossvalidation)
0.4202
Cross-validation details (10-fold Crossvalidation)
0.75 ± 0.0302
Cross-validation details (10-fold Crossvalidation)
1000
Per class
Cross-validation details (10-fold Crossvalidation)
0.7338 ± 0.0391
Per class
Cross-validation details (10-fold Crossvalidation)
0.75 ± 0.0302
Cross-validation details (10-fold Crossvalidation)
0.8813
Cross-validation details (10-fold Crossvalidation)
0.7502 ± 0.0344
Cross-validation details (10-fold Crossvalidation)
0.4583
Cross-validation details (10-fold Crossvalidation)
0.4312 ± 0.0192
Cross-validation details (10-fold Crossvalidation)
0.9409 ± 0.0418
Cross-validation details (10-fold Crossvalidation)
0.6471 ± 0.0421
Cross-validation details (10-fold Crossvalidation)