| Issue | #Downvotes for this reason | By |
|---|
| sklearn.linear_model.logistic.LogisticRegression(34) | 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(34)_C | 1.0 |
| sklearn.linear_model.logistic.LogisticRegression(34)_class_weight | null |
| sklearn.linear_model.logistic.LogisticRegression(34)_dual | false |
| sklearn.linear_model.logistic.LogisticRegression(34)_fit_intercept | true |
| sklearn.linear_model.logistic.LogisticRegression(34)_intercept_scaling | 1 |
| sklearn.linear_model.logistic.LogisticRegression(34)_l1_ratio | null |
| sklearn.linear_model.logistic.LogisticRegression(34)_max_iter | 100 |
| sklearn.linear_model.logistic.LogisticRegression(34)_multi_class | "warn" |
| ` for more details">sklearn.linear_model.logistic.LogisticRegression(34)_n_jobs | null |
| sklearn.linear_model.logistic.LogisticRegression(34)_penalty | "l2" |
| sklearn.linear_model.logistic.LogisticRegression(34)_random_state | 64524 |
| sklearn.linear_model.logistic.LogisticRegression(34)_solver | "warn" |
| sklearn.linear_model.logistic.LogisticRegression(34)_tol | 0.0001 |
| sklearn.linear_model.logistic.LogisticRegression(34)_verbose | 0 |
| sklearn.linear_model.logistic.LogisticRegression(34)_warm_start | false |
0.8261 ± 0.0598 Per class Cross-validation details (10-fold Crossvalidation)
|
0.7564 ± 0.0554 Per class Cross-validation details (10-fold Crossvalidation)
|
0.4529 ± 0.1276 Cross-validation details (10-fold Crossvalidation)
|
0.2812 ± 0.0578 Cross-validation details (10-fold Crossvalidation)
|
0.3365 ± 0.0197 Cross-validation details (10-fold Crossvalidation)
|
0.4545 ± 0.0011 Cross-validation details (10-fold Crossvalidation)
|
0.7656 ± 0.0502 Cross-validation details (10-fold Crossvalidation)
|
768 Per class Cross-validation details (10-fold Crossvalidation) |
0.7601 ± 0.055 Per class Cross-validation details (10-fold Crossvalidation)
|
0.7656 ± 0.0502 Cross-validation details (10-fold Crossvalidation)
|
0.9331 ± 0.0032 Cross-validation details (10-fold Crossvalidation)
|
0.7404 ± 0.044 Cross-validation details (10-fold Crossvalidation)
|
0.4766 ± 0.0011 Cross-validation details (10-fold Crossvalidation)
|
0.4002 ± 0.0288 Cross-validation details (10-fold Crossvalidation)
|
0.8396 ± 0.0613 Cross-validation details (10-fold Crossvalidation)
|
0.7135 ± 0.0661 Cross-validation details (10-fold Crossvalidation)
|