OpenML
10592589

Run 10592589

Task 6 (Supervised Classification) letter Uploaded 23-03-2023 by Takeaki Sakabe
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Flow

sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estima tor=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(43)_add_indicatorfalse
sklearn.impute._base.SimpleImputer(43)_copytrue
sklearn.impute._base.SimpleImputer(43)_fill_valuenull
sklearn.impute._base.SimpleImputer(43)_keep_empty_featuresfalse
sklearn.impute._base.SimpleImputer(43)_missing_valuesNaN
sklearn.impute._base.SimpleImputer(43)_strategy"mean"
sklearn.impute._base.SimpleImputer(43)_verbose"deprecated"
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._logistic.LogisticRegression)(1)_memorynull
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._logistic.LogisticRegression)(1)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}]
sklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,estimator=sklearn.linear_model._logistic.LogisticRegression)(1)_verbosefalse
sklearn.linear_model._logistic.LogisticRegression(9)_C1.0
sklearn.linear_model._logistic.LogisticRegression(9)_class_weightnull
sklearn.linear_model._logistic.LogisticRegression(9)_dualfalse
sklearn.linear_model._logistic.LogisticRegression(9)_fit_intercepttrue
sklearn.linear_model._logistic.LogisticRegression(9)_intercept_scaling1
sklearn.linear_model._logistic.LogisticRegression(9)_l1_rationull
sklearn.linear_model._logistic.LogisticRegression(9)_max_iter100
sklearn.linear_model._logistic.LogisticRegression(9)_multi_class"auto"
sklearn.linear_model._logistic.LogisticRegression(9)_n_jobsnull
sklearn.linear_model._logistic.LogisticRegression(9)_penalty"l2"
sklearn.linear_model._logistic.LogisticRegression(9)_random_state29433
sklearn.linear_model._logistic.LogisticRegression(9)_solver"lbfgs"
sklearn.linear_model._logistic.LogisticRegression(9)_tol0.0001
sklearn.linear_model._logistic.LogisticRegression(9)_verbose0
sklearn.linear_model._logistic.LogisticRegression(9)_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.

18 Evaluation measures

Area under ROC curve
0.978 ± 0.0011
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.7587 ± 0.0068
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.7502 ± 0.0071
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.7626 ± 0.0051
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.0294 ± 0.0005
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.074 ± 0
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.7598 ± 0.0069
Cross-validation details (10-fold Crossvalidation)
Number of instances
20000
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.7588 ± 0.0067
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.7598 ± 0.0069
Cross-validation details (10-fold Crossvalidation)
Prior entropy
4.6998 ± 0
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.3977 ± 0.0071
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.1923 ± 0
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.1166 ± 0.0011
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.6063 ± 0.0056
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.7583 ± 0.007
Cross-validation details (10-fold Crossvalidation)