Run
152

Run 152

Task 801 (Learning Curve) diabetes Uploaded 29-10-2019 by Continuous Integration
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Flow

TESTacfdceae1dsklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.S tandardScaler,dummy=sklearn.dummy.DummyClassifier)(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``.
TESTacfdceae1dsklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,dummy=sklearn.dummy.DummyClassifier)(1)_memorynull
TESTacfdceae1dsklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,dummy=sklearn.dummy.DummyClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "dummy", "step_name": "dummy"}}]
TESTacfdceae1dsklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,dummy=sklearn.dummy.DummyClassifier)(1)_verbosefalse
TESTacfdceae1dsklearn.preprocessing.data.StandardScaler(1)_copytrue
TESTacfdceae1dsklearn.preprocessing.data.StandardScaler(1)_with_meanfalse
TESTacfdceae1dsklearn.preprocessing.data.StandardScaler(1)_with_stdtrue
TESTacfdceae1dsklearn.dummy.DummyClassifier(1)_constantnull
TESTacfdceae1dsklearn.dummy.DummyClassifier(1)_random_state62501
TESTacfdceae1dsklearn.dummy.DummyClassifier(1)_strategy"prior"

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.

16 Evaluation measures

0.4952
Per class
0.0002 ± 0.0006
0.4544 ± 0.0014
0.4545 ± 0.0015
0.651 ± 0.0051
768
Per class
0.651 ± 0.0051
0.9331 ± 0.0046
0.9998 ± 0.0004
0.4766 ± 0.0016
0.4766 ± 0.0016
1 ± 0
0.5