Run
10560465

Run 10560465

Task 167140 (Supervised Classification) dna Uploaded 14-08-2021 by Sergey Redyuk
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Flow

sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.Standar dScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression )(5)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 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 to None.
sklearn.linear_model.logistic.LogisticRegression(36)_C1.0
sklearn.linear_model.logistic.LogisticRegression(36)_class_weightnull
sklearn.linear_model.logistic.LogisticRegression(36)_dualfalse
sklearn.linear_model.logistic.LogisticRegression(36)_fit_intercepttrue
sklearn.linear_model.logistic.LogisticRegression(36)_intercept_scaling1
sklearn.linear_model.logistic.LogisticRegression(36)_max_iter100
sklearn.linear_model.logistic.LogisticRegression(36)_multi_class"ovr"
sklearn.linear_model.logistic.LogisticRegression(36)_n_jobs1
sklearn.linear_model.logistic.LogisticRegression(36)_penalty"l2"
sklearn.linear_model.logistic.LogisticRegression(36)_random_state25192
sklearn.linear_model.logistic.LogisticRegression(36)_solver"liblinear"
sklearn.linear_model.logistic.LogisticRegression(36)_tol0.0001
sklearn.linear_model.logistic.LogisticRegression(36)_verbose0
sklearn.linear_model.logistic.LogisticRegression(36)_warm_startfalse
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression)(5)_steps[{"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "logisticregression", "step_name": "logisticregression"}}]
sklearn.preprocessing.data.StandardScaler(43)_copytrue
sklearn.preprocessing.data.StandardScaler(43)_with_meantrue
sklearn.preprocessing.data.StandardScaler(43)_with_stdtrue

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

0.9895 ± 0.0032
Per class
Cross-validation details (10-fold Crossvalidation)
0.9499 ± 0.0095
Per class
Cross-validation details (10-fold Crossvalidation)
0.9187 ± 0.0154
Cross-validation details (10-fold Crossvalidation)
0.8866 ± 0.0104
Cross-validation details (10-fold Crossvalidation)
0.0576 ± 0.0042
Cross-validation details (10-fold Crossvalidation)
0.41 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.9498 ± 0.0095
Cross-validation details (10-fold Crossvalidation)
3186
Per class
Cross-validation details (10-fold Crossvalidation)
0.9503 ± 0.0094
Per class
Cross-validation details (10-fold Crossvalidation)
0.9498 ± 0.0095
Cross-validation details (10-fold Crossvalidation)
1.4798 ± 0.0014
Cross-validation details (10-fold Crossvalidation)
0.1405 ± 0.0102
Cross-validation details (10-fold Crossvalidation)
0.4527 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
0.1699 ± 0.0116
Cross-validation details (10-fold Crossvalidation)
0.3753 ± 0.0256
Cross-validation details (10-fold Crossvalidation)
0.9472 ± 0.0132
Cross-validation details (10-fold Crossvalidation)