Run
10589545

Run 10589545

Task 34539 (Supervised Classification) Amazon_employee_access Uploaded 29-09-2022 by VAIBHAV JAISWAL
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Flow

sklearn.pipeline.Pipeline(categorical=sklearn.pipeline.Pipeline(Imputer=skl earn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.One HotEncoder),model=sklearn.neighbors._classification.KNeighborsClassifier)(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(30)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(30)_copytrue
sklearn.impute._base.SimpleImputer(30)_fill_value"missing"
sklearn.impute._base.SimpleImputer(30)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(30)_strategy"constant"
sklearn.impute._base.SimpleImputer(30)_verbose0
sklearn.preprocessing._encoders.OneHotEncoder(31)_categories"auto"
sklearn.preprocessing._encoders.OneHotEncoder(31)_dropnull
sklearn.preprocessing._encoders.OneHotEncoder(31)_dtype{"oml-python:serialized_object": "type", "value": "np.float64"}
sklearn.preprocessing._encoders.OneHotEncoder(31)_handle_unknown"ignore"
sklearn.preprocessing._encoders.OneHotEncoder(31)_sparsefalse
sklearn.neighbors._classification.KNeighborsClassifier(13)_algorithm"auto"
sklearn.neighbors._classification.KNeighborsClassifier(13)_leaf_size30
sklearn.neighbors._classification.KNeighborsClassifier(13)_metric"minkowski"
sklearn.neighbors._classification.KNeighborsClassifier(13)_metric_paramsnull
sklearn.neighbors._classification.KNeighborsClassifier(13)_n_jobsnull
sklearn.neighbors._classification.KNeighborsClassifier(13)_n_neighbors5
sklearn.neighbors._classification.KNeighborsClassifier(13)_p2
sklearn.neighbors._classification.KNeighborsClassifier(13)_weights"uniform"
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_memorynull
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "encoder", "step_name": "encoder"}}]
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder)(1)_verbosefalse
sklearn.pipeline.Pipeline(categorical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder),model=sklearn.neighbors._classification.KNeighborsClassifier)(1)_memorynull
sklearn.pipeline.Pipeline(categorical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder),model=sklearn.neighbors._classification.KNeighborsClassifier)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "categorical", "step_name": "categorical"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "model", "step_name": "model"}}]
sklearn.pipeline.Pipeline(categorical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder),model=sklearn.neighbors._classification.KNeighborsClassifier)(1)_verbosefalse

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.

18 Evaluation measures

Area under ROC curve
0.8281 ± 0.014
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9434 ± 0.0036
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.4331 ± 0.0387
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.2308 ± 0.0472
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.072 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1091 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9489 ± 0.0031
Cross-validation details (10-fold Crossvalidation)
Number of instances
32769
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.941 ± 0.004
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9489 ± 0.0031
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.319 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.66 ± 0.0208
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.2335 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.2049 ± 0.0048
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8775 ± 0.0203
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.6788 ± 0.019
Cross-validation details (10-fold Crossvalidation)