| Issue | #Downvotes for this reason | By |
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| 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)_C | 0.001 |
| sklearn.linear_model.logistic.LogisticRegression(39)_class_weight | "balanced" |
| sklearn.linear_model.logistic.LogisticRegression(39)_dual | false |
| sklearn.linear_model.logistic.LogisticRegression(39)_fit_intercept | true |
| sklearn.linear_model.logistic.LogisticRegression(39)_intercept_scaling | 1 |
| sklearn.linear_model.logistic.LogisticRegression(39)_l1_ratio | null |
| sklearn.linear_model.logistic.LogisticRegression(39)_max_iter | 10000 |
| sklearn.linear_model.logistic.LogisticRegression(39)_multi_class | "auto" |
| ` for more details">sklearn.linear_model.logistic.LogisticRegression(39)_n_jobs | null |
| sklearn.linear_model.logistic.LogisticRegression(39)_penalty | "l2" |
| sklearn.linear_model.logistic.LogisticRegression(39)_random_state | 29480 |
| sklearn.linear_model.logistic.LogisticRegression(39)_solver | "lbfgs" |
| sklearn.linear_model.logistic.LogisticRegression(39)_tol | 0.0001 |
| sklearn.linear_model.logistic.LogisticRegression(39)_verbose | 0 |
| sklearn.linear_model.logistic.LogisticRegression(39)_warm_start | false |
0.9513 ± 0.009 Per class Cross-validation details (10-fold Crossvalidation)
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0.8843 ± 0.0352 Per class Cross-validation details (10-fold Crossvalidation)
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0.7935 ± 0.0617 Cross-validation details (10-fold Crossvalidation)
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0.0291 ± 0.0135 Cross-validation details (10-fold Crossvalidation)
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0.4005 ± 0.0045 Cross-validation details (10-fold Crossvalidation)
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0.3798 ± 0.0012 Cross-validation details (10-fold Crossvalidation)
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0.8816 ± 0.0355 Cross-validation details (10-fold Crossvalidation)
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625 Per class Cross-validation details (10-fold Crossvalidation) |
0.8875 ± 0.0344 Per class Cross-validation details (10-fold Crossvalidation)
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0.8816 ± 0.0355 Cross-validation details (10-fold Crossvalidation)
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1.3181 ± 0.0124 Cross-validation details (10-fold Crossvalidation)
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1.0546 ± 0.0115 Cross-validation details (10-fold Crossvalidation)
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0.4356 ± 0.0014 Cross-validation details (10-fold Crossvalidation)
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0.4275 ± 0.0044 Cross-validation details (10-fold Crossvalidation)
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0.9815 ± 0.0096 Cross-validation details (10-fold Crossvalidation)
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0.7676 ± 0.0858 Cross-validation details (10-fold Crossvalidation)
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