Run
10587931

Run 10587931

Task 14951 (Supervised Classification) eeg-eye-state Uploaded 30-08-2022 by Young Lee
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Flow

sklearn.linear_model._logistic.LogisticRegression(6)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(6)_C1.0
sklearn.linear_model._logistic.LogisticRegression(6)_class_weightnull
sklearn.linear_model._logistic.LogisticRegression(6)_dualfalse
sklearn.linear_model._logistic.LogisticRegression(6)_fit_intercepttrue
sklearn.linear_model._logistic.LogisticRegression(6)_intercept_scaling1
sklearn.linear_model._logistic.LogisticRegression(6)_l1_rationull
sklearn.linear_model._logistic.LogisticRegression(6)_max_iter100
sklearn.linear_model._logistic.LogisticRegression(6)_multi_class"auto"
sklearn.linear_model._logistic.LogisticRegression(6)_n_jobsnull
sklearn.linear_model._logistic.LogisticRegression(6)_penalty"l2"
sklearn.linear_model._logistic.LogisticRegression(6)_random_state21051
sklearn.linear_model._logistic.LogisticRegression(6)_solver"lbfgs"
sklearn.linear_model._logistic.LogisticRegression(6)_tol0.0001
sklearn.linear_model._logistic.LogisticRegression(6)_verbose0
sklearn.linear_model._logistic.LogisticRegression(6)_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

0.6705 ± 0.0122
Per class
Cross-validation details (10-fold Crossvalidation)
0.6286 ± 0.0112
Per class
Cross-validation details (10-fold Crossvalidation)
0.2496 ± 0.0219
Cross-validation details (10-fold Crossvalidation)
0.1063 ± 0.0067
Cross-validation details (10-fold Crossvalidation)
0.45 ± 0.0028
Cross-validation details (10-fold Crossvalidation)
0.4948 ± 0
Cross-validation details (10-fold Crossvalidation)
0.6366 ± 0.0103
Cross-validation details (10-fold Crossvalidation)
14980
Per class
Cross-validation details (10-fold Crossvalidation)
0.6346 ± 0.0109
Per class
Cross-validation details (10-fold Crossvalidation)
0.6366 ± 0.0103
Cross-validation details (10-fold Crossvalidation)
0.9924 ± 0.0001
Cross-validation details (10-fold Crossvalidation)
0.9095 ± 0.0056
Cross-validation details (10-fold Crossvalidation)
0.4974 ± 0
Cross-validation details (10-fold Crossvalidation)
0.4746 ± 0.0021
Cross-validation details (10-fold Crossvalidation)
0.9542 ± 0.0043
Cross-validation details (10-fold Crossvalidation)
0.6221 ± 0.0108
Cross-validation details (10-fold Crossvalidation)