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  • 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

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  • 原文地址:https://www.cnblogs.com/rsapaper/p/7612063.html
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