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Update 1.Liner Regression.md #38

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Update 1.Liner Regression.md #38

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2 changes: 1 addition & 1 deletion Machine Learning/Liner Regression/1.Liner Regression.md
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Expand Up @@ -67,7 +67,7 @@ L表示为图中的黑色圆形，随着梯度下降法的不断逼近，与圆

### 5.2 什么场景下用L2正则化

只要数据线性相关，用LinearRegression拟合的不是很好，**需要正则化**，可以考虑使用岭回归(L2), 如何输入特征的维度很高,而且是稀疏线性关系的话， 岭回归就不太合适,考虑使用Lasso回归。
只要数据线性相关，用LinearRegression拟合的不是很好，**需要正则化**，可以考虑使用岭回归(L2), 如果输入特征的维度很高,而且是稀疏线性关系的话， 岭回归就不太合适,考虑使用Lasso回归。

### 5.3 什么是L1正则化(Lasso回归)

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