OpenML
969

Run 969

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

TESTe20d0e003asklearn.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``.
TESTe20d0e003asklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression)(1)_memorynull
TESTe20d0e003asklearn.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"}}]
TESTe20d0e003asklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,regressor=sklearn.linear_model._base.LinearRegression)(1)_verbosefalse
TESTe20d0e003asklearn.impute._base.SimpleImputer(1)_add_indicatorfalse
TESTe20d0e003asklearn.impute._base.SimpleImputer(1)_copytrue
TESTe20d0e003asklearn.impute._base.SimpleImputer(1)_fill_valuenull
TESTe20d0e003asklearn.impute._base.SimpleImputer(1)_missing_valuesNaN
TESTe20d0e003asklearn.impute._base.SimpleImputer(1)_strategy"mean"
TESTe20d0e003asklearn.impute._base.SimpleImputer(1)_verbose0
TESTe20d0e003asklearn.linear_model._base.LinearRegression(1)_copy_Xtrue
TESTe20d0e003asklearn.linear_model._base.LinearRegression(1)_fit_intercepttrue
TESTe20d0e003asklearn.linear_model._base.LinearRegression(1)_n_jobsnull
TESTe20d0e003asklearn.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