Run
10560322

Run 10560322

Task 23 (Supervised Classification) cmc Uploaded 13-08-2021 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_state52213
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

Area under ROC curve
0.6965 ± 0.0373
Per class
Cross-validation details (10-fold Crossvalidation)
F measure
0.5039 ± 0.0352
Per class
Cross-validation details (10-fold Crossvalidation)
Kappa
0.2282 ± 0.0536
Cross-validation details (10-fold Crossvalidation)
KB relative information score
0.1793 ± 0.0217
Cross-validation details (10-fold Crossvalidation)
Mean absolute error
0.3832 ± 0.009
Cross-validation details (10-fold Crossvalidation)
Mean prior absolute error
0.4308 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Weighted recall
0.5119 ± 0.0349
Cross-validation details (10-fold Crossvalidation)
Number of instances
1473
Per class
Cross-validation details (10-fold Crossvalidation)
Precision
0.5051 ± 0.0397
Per class
Cross-validation details (10-fold Crossvalidation)
Predictive accuracy
0.5119 ± 0.0349
Cross-validation details (10-fold Crossvalidation)
Prior entropy
1.539 ± 0.0019
Cross-validation details (10-fold Crossvalidation)
Relative absolute error
0.8895 ± 0.0208
Cross-validation details (10-fold Crossvalidation)
Root mean prior squared error
0.4641 ± 0.0003
Cross-validation details (10-fold Crossvalidation)
Root mean squared error
0.4379 ± 0.0099
Cross-validation details (10-fold Crossvalidation)
Root relative squared error
0.9436 ± 0.0213
Cross-validation details (10-fold Crossvalidation)
Unweighted recall
0.4772 ± 0.036
Cross-validation details (10-fold Crossvalidation)