OpenML
2017

Run 2017

Task 733 (Supervised Regression) quake Uploaded 12-01-2024 by Continuous Integration
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Flow

TEST3ddbf58d14sklearn.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``.
TEST3ddbf58d14sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression)(1)_memorynull
TEST3ddbf58d14sklearn.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"}}]
TEST3ddbf58d14sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression)(1)_verbosefalse
TEST3ddbf58d14sklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TEST3ddbf58d14sklearn.impute._base.SimpleImputer(1)_copytrue
TEST3ddbf58d14sklearn.impute._base.SimpleImputer(1)_fill_valuenull
TEST3ddbf58d14sklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TEST3ddbf58d14sklearn.impute._base.SimpleImputer(1)_strategy"mean"
TEST3ddbf58d14sklearn.impute._base.SimpleImputer(1)_verbose0
TEST3ddbf58d14sklearn.linear_model._base.LinearRegression(1)_copy_Xtrue
TEST3ddbf58d14sklearn.linear_model._base.LinearRegression(1)_fit_intercepttrue
TEST3ddbf58d14sklearn.linear_model._base.LinearRegression(1)_n_jobsnull
TEST3ddbf58d14sklearn.linear_model._base.LinearRegression(1)_normalizefalse

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