Run
10591784

Run 10591784

Task 2295 (Supervised Regression) cholesterol Uploaded 13-01-2023 by Sharath Kumar Reddy Alijarla
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Flow

sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn .impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._v ariance_threshold.VarianceThreshold,decisiontreeregressor=sklearn.tree._cla sses.DecisionTreeRegressor)(2)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.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,decisiontreeregressor=sklearn.tree._classes.DecisionTreeRegressor)(2)_memorynull
sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,decisiontreeregressor=sklearn.tree._classes.DecisionTreeRegressor)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "pipeline", "step_name": "pipeline"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeregressor", "step_name": "decisiontreeregressor"}}]
sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,decisiontreeregressor=sklearn.tree._classes.DecisionTreeRegressor)(2)_verbosefalse
sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer)(2)_memorynull
sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer)(2)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "impute", "step_name": "impute"}}]
sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer)(2)_verbosefalse
sklearn.impute._base.SimpleImputer(38)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(38)_copytrue
sklearn.impute._base.SimpleImputer(38)_fill_valuenull
sklearn.impute._base.SimpleImputer(38)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(38)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(38)_strategy"mean"
sklearn.impute._base.SimpleImputer(38)_verbose"deprecated"
sklearn.feature_selection._variance_threshold.VarianceThreshold(6)_threshold0.0
sklearn.tree._classes.DecisionTreeRegressor(3)_ccp_alpha0.0
sklearn.tree._classes.DecisionTreeRegressor(3)_criterion"squared_error"
sklearn.tree._classes.DecisionTreeRegressor(3)_max_depthnull
sklearn.tree._classes.DecisionTreeRegressor(3)_max_featuresnull
sklearn.tree._classes.DecisionTreeRegressor(3)_max_leaf_nodesnull
sklearn.tree._classes.DecisionTreeRegressor(3)_min_impurity_decrease0.0
sklearn.tree._classes.DecisionTreeRegressor(3)_min_samples_leaf1
sklearn.tree._classes.DecisionTreeRegressor(3)_min_samples_split2
sklearn.tree._classes.DecisionTreeRegressor(3)_min_weight_fraction_leaf0.0
sklearn.tree._classes.DecisionTreeRegressor(3)_random_state51589
sklearn.tree._classes.DecisionTreeRegressor(3)_splitter"best"

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)