| Issue | #Downvotes for this reason | By |
|---|
| sklearn.linear_model._logistic.LogisticRegression(1) | 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(1)_C | 50 |
| sklearn.linear_model._logistic.LogisticRegression(1)_class_weight | null |
| sklearn.linear_model._logistic.LogisticRegression(1)_dual | false |
| sklearn.linear_model._logistic.LogisticRegression(1)_fit_intercept | true |
| sklearn.linear_model._logistic.LogisticRegression(1)_intercept_scaling | 1 |
| sklearn.linear_model._logistic.LogisticRegression(1)_l1_ratio | null |
| sklearn.linear_model._logistic.LogisticRegression(1)_max_iter | 1500 |
| sklearn.linear_model._logistic.LogisticRegression(1)_multi_class | "auto" |
| ` for more details">sklearn.linear_model._logistic.LogisticRegression(1)_n_jobs | null |
| sklearn.linear_model._logistic.LogisticRegression(1)_penalty | "l2" |
| sklearn.linear_model._logistic.LogisticRegression(1)_random_state | 20168 |
| sklearn.linear_model._logistic.LogisticRegression(1)_solver | "lbfgs" |
| sklearn.linear_model._logistic.LogisticRegression(1)_tol | 0.0001 |
| sklearn.linear_model._logistic.LogisticRegression(1)_verbose | 0 |
| sklearn.linear_model._logistic.LogisticRegression(1)_warm_start | false |
0.9953 ± 0.0028 Per class Cross-validation details (10-fold Crossvalidation)
|
0.9718 ± 0.0115 Per class Cross-validation details (10-fold Crossvalidation)
|
0.9436 ± 0.0231 Cross-validation details (10-fold Crossvalidation)
|
0.9094 ± 0.0202 Cross-validation details (10-fold Crossvalidation)
|
0.049 ± 0.0099 Cross-validation details (10-fold Crossvalidation)
|
0.499 ± 0 Cross-validation details (10-fold Crossvalidation)
|
0.9718 ± 0.0115 Cross-validation details (10-fold Crossvalidation)
|
3196 Per class Cross-validation details (10-fold Crossvalidation) |
0.9719 ± 0.0114 Per class Cross-validation details (10-fold Crossvalidation)
|
0.9718 ± 0.0115 Cross-validation details (10-fold Crossvalidation)
|
0.9986 ± 0.0001 Cross-validation details (10-fold Crossvalidation)
|
0.0981 ± 0.0198 Cross-validation details (10-fold Crossvalidation)
|
0.4995 ± 0 Cross-validation details (10-fold Crossvalidation)
|
0.1546 ± 0.0249 Cross-validation details (10-fold Crossvalidation)
|
0.3095 ± 0.0497 Cross-validation details (10-fold Crossvalidation)
|
0.972 ± 0.0115 Cross-validation details (10-fold Crossvalidation)
|