| 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 | 7159 |
| 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 |
0.812 ± 0.0199 Per class Cross-validation details (10-fold Crossvalidation)
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0.7443 ± 0.0203 Per class Cross-validation details (10-fold Crossvalidation)
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0.4348 ± 0.0424 Cross-validation details (10-fold Crossvalidation)
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-0.0122 ± 0.0257 Cross-validation details (10-fold Crossvalidation)
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0.4077 ± 0.007 Cross-validation details (10-fold Crossvalidation)
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0.4147 ± 0.0003 Cross-validation details (10-fold Crossvalidation)
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0.7333 ± 0.0214 Cross-validation details (10-fold Crossvalidation)
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5404 Per class Cross-validation details (10-fold Crossvalidation) |
0.7825 ± 0.0197 Per class Cross-validation details (10-fold Crossvalidation)
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0.7333 ± 0.0214 Cross-validation details (10-fold Crossvalidation)
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0.8732 ± 0.001 Cross-validation details (10-fold Crossvalidation)
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0.983 ± 0.0171 Cross-validation details (10-fold Crossvalidation)
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0.4554 ± 0.0004 Cross-validation details (10-fold Crossvalidation)
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0.4334 ± 0.0073 Cross-validation details (10-fold Crossvalidation)
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0.9519 ± 0.016 Cross-validation details (10-fold Crossvalidation)
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0.7473 ± 0.0244 Cross-validation details (10-fold Crossvalidation)
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