ridge regression 无惩罚，导致预测结果空间过大而无实用价值

【 biased regression methods to reduce variance---通过偏回归来减小方差】

https://onlinecourses.science.psu.edu/stat857/node/137

• Introducing biased regression methods to reduce variance
• Implementation of Ridge and Lasso regression

https://onlinecourses.science.psu.edu/stat857/node/155

【无惩罚，导致预测结果空间过大而无实用价值】

【fitting the full model without penalization will result in large prediction intervals】

Motivation: too many predictors

• It is not unusual to see the number of input variables greatly exceed the number of observations, e.g. micro-array data analysis, environmental pollution studies.

• With many predictors, fitting the full model without penalization will result in large prediction intervals, and LS regression estimator may not uniquely exist.

https://gerardnico.com/wiki/data_mining/lasso

ridge regression 无惩罚，导致预测结果空间过大而无实用价值

原文：http://www.cnblogs.com/yuanjiangw/p/7612063.html