为何Scikit-learn中Ridge与Lasso回归需设置random_state参数？

Great question! At first glance, you might think linear regression variants like Lasso and Ridge are totally deterministic—so why would they need a random_state? Let's break down the key scenarios where this parameter matters:

• Randomized Solvers (SAG/SAGA)
  When you use the sag or saga solvers (popular for large datasets), these are stochastic gradient descent variants. They randomly select subsets of training samples in each iteration to compute gradient updates. Without a fixed random_state, each run will pick different sample subsets, leading to slightly different model parameters even with the same data and hyperparameters. Setting random_state locks in this randomness, making your model training fully reproducible—critical for debugging, sharing results, or comparing different hyperparameter settings.
  Example: Lasso(solver='saga', alpha=0.1, random_state=42) ensures the same sequence of sample selections every time you train.

• Cross-Validation Variants (LassoCV/RidgeCV)
  If you're using the cross-validation versions of these models (like LassoCV to automatically find the best alpha), the process often involves random splits of the training data for cross-validation (especially if you enable shuffling). The random_state parameter fixes how these splits are generated, so you get the same cross-validation scores and selected alpha value every run. This eliminates variability from random data splitting, making your model selection consistent.

• API Consistency & Edge Cases
  Even when using deterministic solvers (like the default auto which picks Cholesky or SVD for small datasets), Scikit-learn includes random_state in the class signature for API consistency. This way, you don't have to remember which solver requires the parameter—you can set it universally if you plan to switch to a randomized solver later. Additionally, edge cases like using SGDRegressor to mimic Lasso/Ridge (with penalty='l1' or 'l2') rely on random initialization, where random_state ensures consistent starting points for gradient descent.

Note: If you're using a fully deterministic solver and not doing cross-validation with random splits, the random_state parameter will be ignored—so no harm in setting it anyway for future-proofing!

内容的提问来源于stack exchange，提问作者Krishna