OpenML
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Run 524

Task 733 (Supervised Regression) quake Uploaded 11-01-2024 by Continuous Integration
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Flow

TESTb100759e6fsklearn.pipeline.Pipeline(imputer=sklearn.impute._base.Simple Imputer,regressor=sklearn.linear_model._base.LinearRegression)(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``.
TESTb100759e6fsklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression)(1)_memorynull
TESTb100759e6fsklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "regressor", "step_name": "regressor"}}]
TESTb100759e6fsklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression)(1)_verbosefalse
TESTb100759e6fsklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TESTb100759e6fsklearn.impute._base.SimpleImputer(1)_copytrue
TESTb100759e6fsklearn.impute._base.SimpleImputer(1)_fill_valuenull
TESTb100759e6fsklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TESTb100759e6fsklearn.impute._base.SimpleImputer(1)_strategy"mean"
TESTb100759e6fsklearn.impute._base.SimpleImputer(1)_verbose0
TESTb100759e6fsklearn.linear_model._base.LinearRegression(1)_copy_Xtrue
TESTb100759e6fsklearn.linear_model._base.LinearRegression(1)_fit_intercepttrue
TESTb100759e6fsklearn.linear_model._base.LinearRegression(1)_n_jobsnull
TESTb100759e6fsklearn.linear_model._base.LinearRegression(1)_normalizefalse
TESTb100759e6fsklearn.linear_model._base.LinearRegression(1)_positivefalse

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.

7 Evaluation measures

0.1486 ± 0.0063
0.1491 ± 0.0067
2178
0.9962 ± 0.0102
0.1894 ± 0.0107
0.189 ± 0.0103
0.9981 ± 0.0047