| Issue | #Downvotes for this reason | By |
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| sklearn.linear_model._logistic.LogisticRegression(8) | 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(8)_C | 1.0 |
| sklearn.linear_model._logistic.LogisticRegression(8)_class_weight | null |
| sklearn.linear_model._logistic.LogisticRegression(8)_dual | false |
| sklearn.linear_model._logistic.LogisticRegression(8)_fit_intercept | true |
| sklearn.linear_model._logistic.LogisticRegression(8)_intercept_scaling | 1 |
| sklearn.linear_model._logistic.LogisticRegression(8)_l1_ratio | null |
| sklearn.linear_model._logistic.LogisticRegression(8)_max_iter | 100 |
| sklearn.linear_model._logistic.LogisticRegression(8)_multi_class | "auto" |
| ` for more details">sklearn.linear_model._logistic.LogisticRegression(8)_n_jobs | null |
| sklearn.linear_model._logistic.LogisticRegression(8)_penalty | "l2" |
| sklearn.linear_model._logistic.LogisticRegression(8)_random_state | 250 |
| sklearn.linear_model._logistic.LogisticRegression(8)_solver | "lbfgs" |
| sklearn.linear_model._logistic.LogisticRegression(8)_tol | 0.0001 |
| sklearn.linear_model._logistic.LogisticRegression(8)_verbose | 0 |
| sklearn.linear_model._logistic.LogisticRegression(8)_warm_start | false |
0.6721 ± 0.0092 Per class Cross-validation details (10-fold Crossvalidation)
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0.6288 ± 0.0076 Per class Cross-validation details (10-fold Crossvalidation)
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0.25 ± 0.0154 Cross-validation details (10-fold Crossvalidation)
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0.1072 ± 0.004 Cross-validation details (10-fold Crossvalidation)
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0.4496 ± 0.0016 Cross-validation details (10-fold Crossvalidation)
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0.4948 ± 0 Cross-validation details (10-fold Crossvalidation)
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0.6368 ± 0.0078 Cross-validation details (10-fold Crossvalidation)
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14980 Per class Cross-validation details (10-fold Crossvalidation) |
0.6348 ± 0.0087 Per class Cross-validation details (10-fold Crossvalidation)
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0.6368 ± 0.0078 Cross-validation details (10-fold Crossvalidation)
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0.9924 ± 0.0001 Cross-validation details (10-fold Crossvalidation)
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0.9088 ± 0.0032 Cross-validation details (10-fold Crossvalidation)
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0.4974 ± 0 Cross-validation details (10-fold Crossvalidation)
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0.4744 ± 0.0018 Cross-validation details (10-fold Crossvalidation)
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0.9539 ± 0.0036 Cross-validation details (10-fold Crossvalidation)
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0.6223 ± 0.0075 Cross-validation details (10-fold Crossvalidation)
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