Day_039_HW.ipynb

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    "## [作業重點]\n",
    "清楚了解 L1, L2 的意義與差異為何，並了解 LASSO 與 Ridge 之間的差異與使用情境"
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    "## 作業"
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    "請閱讀相關文獻，並回答下列問題\n",
    "\n",
    "[脊回歸 (Ridge Regression)](https://blog.csdn.net/daunxx/article/details/51578787)\n",
    "[Linear, Ridge, Lasso Regression 本質區別](https://www.zhihu.com/question/38121173)\n",
    "\n",
    "1. LASSO 回歸可以被用來作為 Feature selection 的工具，請了解 LASSO 模型為什麼可用來作 Feature selection\n",
    "2. 當自變數 (X) 存在高度共線性時，Ridge Regression 可以處理這樣的問題嗎?\n"
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    "> 1.LASSO迴歸為 Linear Regression 加上 L1 ，L1正規化有稀疏解可以讓許多w為0，因此可以用來進行特徵選取  \n",
    " 2.可以，脊迴歸是一種修改最小平方法，允許有偏估計量、減少雜訊干擾，進而改善多元共線性的方法"
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   "source": [
    "![Ridge Lasso](https://pic3.zhimg.com/80/v2-2a88e2acc009fa4de3edeb51e683ca02_hd.jpg)"
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