Run
10434491

Run 10434491

Task 10101 (Supervised Classification) blood-transfusion-service-center Uploaded 17-12-2019 by George Volkov
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Flow

sklearn.linear_model.logistic.LogisticRegression(33)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(33)_C1
sklearn.linear_model.logistic.LogisticRegression(33)_class_weightnull
sklearn.linear_model.logistic.LogisticRegression(33)_dualfalse
sklearn.linear_model.logistic.LogisticRegression(33)_fit_intercepttrue
sklearn.linear_model.logistic.LogisticRegression(33)_intercept_scaling1
sklearn.linear_model.logistic.LogisticRegression(33)_l1_rationull
sklearn.linear_model.logistic.LogisticRegression(33)_max_iter100
sklearn.linear_model.logistic.LogisticRegression(33)_multi_class"warn"
` for more details">sklearn.linear_model.logistic.LogisticRegression(33)_n_jobsnull
sklearn.linear_model.logistic.LogisticRegression(33)_penalty"l2"
sklearn.linear_model.logistic.LogisticRegression(33)_random_state1248
sklearn.linear_model.logistic.LogisticRegression(33)_solver"warn"
sklearn.linear_model.logistic.LogisticRegression(33)_tol0.0001
sklearn.linear_model.logistic.LogisticRegression(33)_verbose0
sklearn.linear_model.logistic.LogisticRegression(33)_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

0.7516 ± 0.0476
Per class
Cross-validation details (10-fold Crossvalidation)
0.7074 ± 0.0388
Per class
Cross-validation details (10-fold Crossvalidation)
0.1291 ± 0.1123
Cross-validation details (10-fold Crossvalidation)
0.097 ± 0.0621
Cross-validation details (10-fold Crossvalidation)
0.3091 ± 0.0179
Cross-validation details (10-fold Crossvalidation)
0.363 ± 0.0023
Cross-validation details (10-fold Crossvalidation)
0.7714 ± 0.0274
Cross-validation details (10-fold Crossvalidation)
748
Per class
Cross-validation details (10-fold Crossvalidation)
0.737 ± 0.0874
Per class
Cross-validation details (10-fold Crossvalidation)
0.7714 ± 0.0274
Cross-validation details (10-fold Crossvalidation)
0.7916 ± 0.0072
Cross-validation details (10-fold Crossvalidation)
0.8515 ± 0.0486
Cross-validation details (10-fold Crossvalidation)
0.4258 ± 0.0027
Cross-validation details (10-fold Crossvalidation)
0.3928 ± 0.0185
Cross-validation details (10-fold Crossvalidation)
0.9223 ± 0.0426
Cross-validation details (10-fold Crossvalidation)
0.5467 ± 0.0422
Cross-validation details (10-fold Crossvalidation)