Run
10560599

Run 10560599

Task 10093 (Supervised Classification) banknote-authentication Uploaded 14-08-2021 by Sergey Redyuk
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Flow

sklearn.linear_model.logistic.LogisticRegression(39)Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the cross-entropy loss if the 'multi_class' option is set to 'multinomial'. (Currently the 'multinomial' option is supported only by the 'lbfgs', 'sag', 'saga' and 'newton-cg' solvers.) This class implements regularized logistic regression using the 'liblinear' library, 'newton-cg', 'sag', 'saga' and 'lbfgs' solvers. **Note that regularization is applied by default**. It can handle both dense and sparse input. Use C-ordered arrays or CSR matrices containing 64-bit floats for optimal performance; any other input format will be converted (and copied). The 'newton-cg', 'sag', and 'lbfgs' solvers support only L2 regularization with primal formulation, or no regularization. The 'liblinear' solver supports both L1 and L2 regularization, with a dual formulation only for the L2 penalty. The Elastic-Net regularization is only su...
sklearn.linear_model.logistic.LogisticRegression(39)_C0.001
sklearn.linear_model.logistic.LogisticRegression(39)_class_weight"balanced"
sklearn.linear_model.logistic.LogisticRegression(39)_dualfalse
sklearn.linear_model.logistic.LogisticRegression(39)_fit_intercepttrue
sklearn.linear_model.logistic.LogisticRegression(39)_intercept_scaling1
sklearn.linear_model.logistic.LogisticRegression(39)_l1_rationull
sklearn.linear_model.logistic.LogisticRegression(39)_max_iter10000
sklearn.linear_model.logistic.LogisticRegression(39)_multi_class"auto"
` for more details">sklearn.linear_model.logistic.LogisticRegression(39)_n_jobsnull
sklearn.linear_model.logistic.LogisticRegression(39)_penalty"l2"
sklearn.linear_model.logistic.LogisticRegression(39)_random_state24203
sklearn.linear_model.logistic.LogisticRegression(39)_solver"lbfgs"
sklearn.linear_model.logistic.LogisticRegression(39)_tol0.0001
sklearn.linear_model.logistic.LogisticRegression(39)_verbose0
sklearn.linear_model.logistic.LogisticRegression(39)_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

0.9967 ± 0.0025
Per class
Cross-validation details (10-fold Crossvalidation)
0.9745 ± 0.013
Per class
Cross-validation details (10-fold Crossvalidation)
0.9486 ± 0.0261
Cross-validation details (10-fold Crossvalidation)
0.6167 ± 0.0189
Cross-validation details (10-fold Crossvalidation)
0.2216 ± 0.0087
Cross-validation details (10-fold Crossvalidation)
0.4939 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.9745 ± 0.013
Cross-validation details (10-fold Crossvalidation)
1372
Per class
Cross-validation details (10-fold Crossvalidation)
0.9753 ± 0.0124
Per class
Cross-validation details (10-fold Crossvalidation)
0.9745 ± 0.013
Cross-validation details (10-fold Crossvalidation)
0.9911 ± 0.0004
Cross-validation details (10-fold Crossvalidation)
0.4486 ± 0.0176
Cross-validation details (10-fold Crossvalidation)
0.4969 ± 0.0002
Cross-validation details (10-fold Crossvalidation)
0.2511 ± 0.0113
Cross-validation details (10-fold Crossvalidation)
0.5053 ± 0.0228
Cross-validation details (10-fold Crossvalidation)
0.9764 ± 0.0125
Cross-validation details (10-fold Crossvalidation)