官术网_书友最值得收藏!

  • Advanced Machine Learning with R
  • Cory Lesmeister Dr. Sunil Kumar Chinnamgari
  • 208字
  • 2021-06-24 14:24:39

Elastic net

The power of elastic net is that it performs feature extraction, unlike ridge regression, and it'll group the features that LASSO fails to do. Again, LASSO will tend to select one feature from a group of correlated ones and ignore the rest. Elastic net does this by including a mixing parameter, alpha, in conjunction with lambda. Alpha will be between 0 and 1, and as before, lambda will regulate the size of the penalty. Please note that an alpha of zero is equal to ridge regression and an alpha of 1 is equivalent to LASSO. Essentially, we're blending the L1 and L2 penalties by including a second tuning parameter with a quadratic (squared) term of the beta coefficients. We'll end up with the goal of minimizing (RSS + λ[(1-alpha) (sum|Bj|2)/2 + alpha (sum |Bj|)])/N).

Let's put these techniques to the test. We'll utilize a dataset I created to demonstrate the methods. In the next section, I'll discuss how I created the dataset with a few predictive features and some noise features, including those with high correlation. I recommend that, once you feel comfortable with this chapter's content, you go back and apply them to the data examined in the prior two chapters, comparing performance.

主站蜘蛛池模板: 通道| 武川县| 永顺县| 石家庄市| 信阳市| 多伦县| 微山县| 德化县| 邳州市| 桐乡市| 紫阳县| 古丈县| 峨山| 老河口市| 隆尧县| 合江县| 邯郸县| 财经| 莒南县| 曲靖市| 涿鹿县| 桂东县| 都江堰市| 手游| 宜兰市| 克拉玛依市| 民县| 义马市| 金乡县| 临清市| 于都县| 松原市| 达拉特旗| 佳木斯市| 永泰县| 谢通门县| 柳州市| 罗田县| 泸溪县| 崇义县| 淄博市|