| Issue | #Downvotes for this reason | By |
|---|
| sklearn.linear_model._ridge.Ridge(1) | Linear least squares with l2 regularization. Minimizes the objective function:: ||y - Xw||^2_2 + alpha * ||w||^2_2 This model solves a regression model where the loss function is the linear least squares function and regularization is given by the l2-norm. Also known as Ridge Regression or Tikhonov regularization. This estimator has built-in support for multi-variate regression (i.e., when y is a 2d-array of shape (n_samples, n_targets)). |
| sklearn.linear_model._ridge.Ridge(1)_alpha | 0.5 |
| sklearn.linear_model._ridge.Ridge(1)_copy_X | true |
| sklearn.linear_model._ridge.Ridge(1)_fit_intercept | true |
| sklearn.linear_model._ridge.Ridge(1)_max_iter | null |
| sklearn.linear_model._ridge.Ridge(1)_normalize | false |
| sklearn.linear_model._ridge.Ridge(1)_random_state | 10482 |
| sklearn.linear_model._ridge.Ridge(1)_solver | "auto" |
| sklearn.linear_model._ridge.Ridge(1)_tol | 0.001 |
3.5303 ± 0.6322 Cross-validation details (10-fold Crossvalidation)
|
6.8603 ± 0.7267 Cross-validation details (10-fold Crossvalidation)
|
252 Cross-validation details (10-fold Crossvalidation) |
0.5146 ± 0.0839 Cross-validation details (10-fold Crossvalidation)
|
8.3521 ± 0.9478 Cross-validation details (10-fold Crossvalidation)
|
4.3145 ± 0.7333 Cross-validation details (10-fold Crossvalidation)
|
0.5166 ± 0.0721 Cross-validation details (10-fold Crossvalidation)
|