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Task 801 (Learning Curve) diabetes Uploaded 12-01-2024 by Continuous Integration
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Flow

TEST4a4cda6aafsklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data. StandardScaler,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`. For an example use case of `Pipeline` combined with :class:`~sklearn.model_selection.GridSearchCV`, refer to :ref:`sphx_glr_auto_examples_compose_plot_compare_reduction.py`. The example :ref:`sphx_glr_auto_exampl...
TEST4a4cda6aafsklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,dummy=sklearn.dummy.DummyClassifier)(1)_memorynull
TEST4a4cda6aafsklearn.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"}}]
TEST4a4cda6aafsklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,dummy=sklearn.dummy.DummyClassifier)(1)_verbosefalse
TEST4a4cda6aafsklearn.preprocessing._data.StandardScaler(1)_copytrue
TEST4a4cda6aafsklearn.preprocessing._data.StandardScaler(1)_with_meanfalse
TEST4a4cda6aafsklearn.preprocessing._data.StandardScaler(1)_with_stdtrue
TEST4a4cda6aafsklearn.dummy.DummyClassifier(1)_constantnull
TEST4a4cda6aafsklearn.dummy.DummyClassifier(1)_random_state62501
TEST4a4cda6aafsklearn.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