Run
32149

Run 32149

Task 801 (Learning Curve) diabetes Uploaded 30-03-2021 by Continuous Integration
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Flow

TEST94d4222ab5sklearn.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``.
TEST94d4222ab5sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,dummy=sklearn.dummy.DummyClassifier)(1)_memorynull
TEST94d4222ab5sklearn.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"}}]
TEST94d4222ab5sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,dummy=sklearn.dummy.DummyClassifier)(1)_verbosefalse
TEST94d4222ab5sklearn.preprocessing.data.StandardScaler(1)_copytrue
TEST94d4222ab5sklearn.preprocessing.data.StandardScaler(1)_with_meanfalse
TEST94d4222ab5sklearn.preprocessing.data.StandardScaler(1)_with_stdtrue
TEST94d4222ab5sklearn.dummy.DummyClassifier(1)_constantnull
TEST94d4222ab5sklearn.dummy.DummyClassifier(1)_random_state62501
TEST94d4222ab5sklearn.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