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Update 1.Liner Regression.md #37

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4 changes: 2 additions & 2 deletions Machine Learning/Liner Regression/1.Liner Regression.md
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Expand Up @@ -37,13 +37,13 @@ w叫做x的系数，b叫做偏置项。

利用**梯度下降法**找到最小值点，也就是最小误差，最后把 w 和 b 给求出来。

## 5. 过拟合、欠拟合如何解决
## 5. 过拟合如何解决 -- 正则化（方法之一）

使用正则化项，也就是给loss function加上一个参数项，正则化项有**L1正则化、L2正则化、ElasticNet**。加入这个正则化项好处：

- 控制参数幅度，不让模型“无法无天”。
- 限制参数搜索空间
- 解决欠拟合与过拟合的问题。
- 解决过拟合的问题。

### 5.1 什么是L2正则化(岭回归)

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