Day_039_HW-LASSO, Ridge Regression.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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   "metadata": {},
   "source": [
    "## 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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   "source": [
    "# 當模型的目標函數加入 L1 正則化後，會傾向讓用不到的特徵的權重降為 ０，因此實務上可以替我們做到 feature selection 的功用\n",
    "# from IPython.display import YouTubeVideo\n",
    "# YouTubeVideo('PeQeKeeGu3A', width=720, height=480)\n",
    "# 2019/10/16"
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "# L1 norm就是絕對值相加，又稱曼哈頓距離;\n",
    "# L2 norm就是歐幾里德距離之和...就是樣本和標籤之差的平方之和\n",
    "# 原文網址：https://kknews.cc/news/e4l6gar.html\n",
    "# 2019/10/16"
   ]
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