OpenML
10591810

Run 10591810

Task 211993 (Supervised Regression) titanic_2 Uploaded 29-01-2023 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,randomforestregressor=sklearn.ensemble. _forest.RandomForestRegressor)(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.impute._base.SimpleImputer(30)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(30)_copytrue
sklearn.impute._base.SimpleImputer(30)_fill_valuenull
sklearn.impute._base.SimpleImputer(30)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(30)_strategy"mean"
sklearn.impute._base.SimpleImputer(30)_verbose0
sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer)(3)_memorynull
sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer)(3)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "impute", "step_name": "impute"}}]
sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer)(3)_verbosefalse
sklearn.feature_selection._variance_threshold.VarianceThreshold(7)_threshold0.0
sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestregressor=sklearn.ensemble._forest.RandomForestRegressor)(2)_memorynull
sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestregressor=sklearn.ensemble._forest.RandomForestRegressor)(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": "randomforestregressor", "step_name": "randomforestregressor"}}]
sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestregressor=sklearn.ensemble._forest.RandomForestRegressor)(2)_verbosefalse
sklearn.ensemble._forest.RandomForestRegressor(3)_bootstrap"False"
sklearn.ensemble._forest.RandomForestRegressor(3)_ccp_alpha0.0
sklearn.ensemble._forest.RandomForestRegressor(3)_criterion"friedman_mse"
sklearn.ensemble._forest.RandomForestRegressor(3)_max_depthnull
sklearn.ensemble._forest.RandomForestRegressor(3)_max_features0.4555058146520773
sklearn.ensemble._forest.RandomForestRegressor(3)_max_leaf_nodesnull
sklearn.ensemble._forest.RandomForestRegressor(3)_max_samplesnull
sklearn.ensemble._forest.RandomForestRegressor(3)_min_impurity_decrease0.0
sklearn.ensemble._forest.RandomForestRegressor(3)_min_samples_leaf14
sklearn.ensemble._forest.RandomForestRegressor(3)_min_samples_split17
sklearn.ensemble._forest.RandomForestRegressor(3)_min_weight_fraction_leaf0.0
sklearn.ensemble._forest.RandomForestRegressor(3)_n_estimators100
sklearn.ensemble._forest.RandomForestRegressor(3)_n_jobsnull
sklearn.ensemble._forest.RandomForestRegressor(3)_oob_scorefalse
sklearn.ensemble._forest.RandomForestRegressor(3)_random_state51727
sklearn.ensemble._forest.RandomForestRegressor(3)_verbose0
sklearn.ensemble._forest.RandomForestRegressor(3)_warm_startfalse

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
0.473 ± 0.0124
Cross-validation details (10-fold Crossvalidation)
Number of instances
891
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4863 ± 0.0129
Cross-validation details (10-fold Crossvalidation)