Run
9352

Run 9352

Task 119 (Supervised Classification) diabetes Uploaded 25-11-2022 by Continuous Integration
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Flow

TESTf987988399sklearn.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 to None.
TESTf987988399sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing.data.StandardScaler,dummy=sklearn.dummy.DummyClassifier)(1)_memorynull
TESTf987988399sklearn.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"}}]
TESTf987988399sklearn.preprocessing.data.StandardScaler(1)_copytrue
TESTf987988399sklearn.preprocessing.data.StandardScaler(1)_with_meanfalse
TESTf987988399sklearn.preprocessing.data.StandardScaler(1)_with_stdtrue
TESTf987988399sklearn.dummy.DummyClassifier(1)_constantnull
TESTf987988399sklearn.dummy.DummyClassifier(1)_random_state62501
TESTf987988399sklearn.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