Run
10560790

Run 10560790

Task 2295 (Supervised Regression) cholesterol Uploaded 27-08-2021 by Victorien Fandos
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Flow

sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer,std_sca le=sklearn.preprocessing._data.StandardScaler,knn=sklearn.neighbors._regres sion.KNeighborsRegressor)(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``.
sklearn.impute._base.SimpleImputer(25)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(25)_copytrue
sklearn.impute._base.SimpleImputer(25)_fill_valuenull
sklearn.impute._base.SimpleImputer(25)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(25)_strategy"mean"
sklearn.impute._base.SimpleImputer(25)_verbose0
sklearn.preprocessing._data.StandardScaler(9)_copytrue
sklearn.preprocessing._data.StandardScaler(9)_with_meantrue
sklearn.preprocessing._data.StandardScaler(9)_with_stdtrue
sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer,std_scale=sklearn.preprocessing._data.StandardScaler,knn=sklearn.neighbors._regression.KNeighborsRegressor)(1)_memorynull
sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer,std_scale=sklearn.preprocessing._data.StandardScaler,knn=sklearn.neighbors._regression.KNeighborsRegressor)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "impute", "step_name": "impute"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "std_scale", "step_name": "std_scale"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "knn", "step_name": "knn"}}]
sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer,std_scale=sklearn.preprocessing._data.StandardScaler,knn=sklearn.neighbors._regression.KNeighborsRegressor)(1)_verbosefalse
sklearn.neighbors._regression.KNeighborsRegressor(3)_algorithm"auto"
sklearn.neighbors._regression.KNeighborsRegressor(3)_leaf_size30
sklearn.neighbors._regression.KNeighborsRegressor(3)_metric"minkowski"
sklearn.neighbors._regression.KNeighborsRegressor(3)_metric_paramsnull
sklearn.neighbors._regression.KNeighborsRegressor(3)_n_jobsnull
sklearn.neighbors._regression.KNeighborsRegressor(3)_n_neighbors9
sklearn.neighbors._regression.KNeighborsRegressor(3)_p2
sklearn.neighbors._regression.KNeighborsRegressor(3)_weights"uniform"

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

Mean prior absolute error
39.3139 ± 6.092
Cross-validation details (10-fold Crossvalidation)
Number of instances
303
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
51.6914 ± 10.5853
Cross-validation details (10-fold Crossvalidation)