Run
10589543

Run 10589543

Task 34539 (Supervised Classification) Amazon_employee_access Uploaded 28-09-2022 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.linear_model._logistic.LogisticRegression)(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.linear_model._logistic.LogisticRegression(7)_C1.0
sklearn.linear_model._logistic.LogisticRegression(7)_class_weightnull
sklearn.linear_model._logistic.LogisticRegression(7)_dualfalse
sklearn.linear_model._logistic.LogisticRegression(7)_fit_intercepttrue
sklearn.linear_model._logistic.LogisticRegression(7)_intercept_scaling1
sklearn.linear_model._logistic.LogisticRegression(7)_l1_rationull
sklearn.linear_model._logistic.LogisticRegression(7)_max_iter2000
sklearn.linear_model._logistic.LogisticRegression(7)_multi_class"auto"
sklearn.linear_model._logistic.LogisticRegression(7)_n_jobsnull
sklearn.linear_model._logistic.LogisticRegression(7)_penalty"l2"
sklearn.linear_model._logistic.LogisticRegression(7)_random_state0
sklearn.linear_model._logistic.LogisticRegression(7)_solver"lbfgs"
sklearn.linear_model._logistic.LogisticRegression(7)_tol0.0001
sklearn.linear_model._logistic.LogisticRegression(7)_verbose0
sklearn.linear_model._logistic.LogisticRegression(7)_warm_startfalse
sklearn.pipeline.Pipeline(categorical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder),model=sklearn.linear_model._logistic.LogisticRegression)(1)_memorynull
sklearn.pipeline.Pipeline(categorical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,encoder=sklearn.preprocessing._encoders.OneHotEncoder),model=sklearn.linear_model._logistic.LogisticRegression)(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.linear_model._logistic.LogisticRegression)(1)_verbosefalse
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

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
0.8669 ± 0.0117
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.9335 ± 0.0028
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2735 ± 0.0336
Cross-validation details (10-fold Crossvalidation)
KB relative information score
-0.1036 ± 0.026
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0818 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.1091 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.9479 ± 0.0021
Cross-validation details (10-fold Crossvalidation)
Number of instances
32769
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.9366 ± 0.0048
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.9479 ± 0.0021
Cross-validation details (10-fold Crossvalidation)
Prior entropy
0.319 ± 0.0006
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.7493 ± 0.0122
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
0.201 ± 0.0037
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.8606 ± 0.0159
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.5899 ± 0.0121
Cross-validation details (10-fold Crossvalidation)