Run
10591811

Run 10591811

Task 211993 (Supervised Regression) titanic_2 Uploaded 29-01-2023 by Sharath Kumar Reddy Alijarla
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
Issue #Downvotes for this reason By


Flow

sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn .impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._v ariance_threshold.VarianceThreshold,kneighborsregressor=sklearn.neighbors._ regression.KNeighborsRegressor)(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,kneighborsregressor=sklearn.neighbors._regression.KNeighborsRegressor)(2)_memorynull
sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,kneighborsregressor=sklearn.neighbors._regression.KNeighborsRegressor)(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": "kneighborsregressor", "step_name": "kneighborsregressor"}}]
sklearn.pipeline.Pipeline(pipeline=sklearn.pipeline.Pipeline(impute=sklearn.impute._base.SimpleImputer),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,kneighborsregressor=sklearn.neighbors._regression.KNeighborsRegressor)(2)_verbosefalse
sklearn.neighbors._regression.KNeighborsRegressor(5)_algorithm"auto"
sklearn.neighbors._regression.KNeighborsRegressor(5)_leaf_size30
sklearn.neighbors._regression.KNeighborsRegressor(5)_metric"minkowski"
sklearn.neighbors._regression.KNeighborsRegressor(5)_metric_paramsnull
sklearn.neighbors._regression.KNeighborsRegressor(5)_n_jobsnull
sklearn.neighbors._regression.KNeighborsRegressor(5)_n_neighbors56
sklearn.neighbors._regression.KNeighborsRegressor(5)_p1
sklearn.neighbors._regression.KNeighborsRegressor(5)_weights"distance"

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)