岭回归与套索回归

Question

套索回归还是弹性网回归总是比岭回归好？在

我是机器学习的新手。我在一些数据集上进行了这些回归，我总是得到相同的结果，即均方误差在套索回归中是最小的。这仅仅是巧合还是真的？在

Answer 1

每个问题都不一样。在套索回归中，算法试图去除那些听上去没有任何用处的额外特征，因为我们可以用较少的数据很好地进行训练，但处理有点困难，但在岭回归中，算法试图使这些额外的特征不那么有效，但并没有完全删除它们更容易处理。在

Answer 2

我认为这个问题可能更适合交叉验证分论坛。在

关于这个话题，詹姆斯、维滕、黑斯蒂和蒂比拉尼在他们的《统计学习导论》一书中写道：

These two examples illustrate that neither ridge regression nor the lasso will universally dominate the other. In general, one might expect the lasso to perform better in a setting where a relatively small number of predictorshave substantial coefficients, and the remaining predictors have coefficients that are very small or that equal zero. Ridge regression will perform better when the response is a function of many predictors, all with coefficients of roughly equal size. However, the number of predictors that is related to the response is never known apriori for real data sets. A technique such as cross-validation can be used in order to determine which approach is betteron a particular data set. (chapter 6.2)