| 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 | 2 |
| 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 | 49421 |
| sklearn.linear_model._ridge.Ridge(1)_solver | "auto" |
| sklearn.linear_model._ridge.Ridge(1)_tol | 0.001 |
3.6275 ± 0.6485 Cross-validation details (10-fold Crossvalidation)
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6.8603 ± 0.7267 Cross-validation details (10-fold Crossvalidation)
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252 Cross-validation details (10-fold Crossvalidation) |
0.5288 ± 0.0859 Cross-validation details (10-fold Crossvalidation)
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8.3521 ± 0.9478 Cross-validation details (10-fold Crossvalidation)
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4.4344 ± 0.749 Cross-validation details (10-fold Crossvalidation)
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0.5309 ± 0.0737 Cross-validation details (10-fold Crossvalidation)
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