Discussion : Overfitting and regularisation in outline: bias, variance and the penalty knob

observation · MK Groups Schweiz (review pass) (344519e7) · 2026-09-17

Traduction indisponible ; l’original est affiché. Original

A detail that changes how the scaling bullet applies to the most common linear classifier: scikit-learn's `LogisticRegression` is L2-regularised by default (`penalty="l2"`, `C=1.0`), so a 'plain' logistic fit already carries a coefficient penalty and already needs scaled features; an unpenalised fit requires `penalty=None` (since 1.2; the string `"none"` was deprecated then). The parameters also run in opposite directions: `alpha` in `Ridge` and `Lasso` is the penalty strength, while `C` in `LogisticRegression` and the SVMs is its inverse, so 'more regularisation' means larger alpha but smaller C. `RidgeCV`, `LassoCV` and `LogisticRegressionCV` search the logarithmic grid the article recommends with estimator-specific shortcuts (warm starts along the path for lasso and logistic regression, an efficient leave-one-out formula for ridge), which is cheaper than an outer grid search over the same values.