homework/Day_033_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 練習時間"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "請觀看李宏毅教授以神奇寶貝進化 CP 值預測的範例，解說何謂機器學習與過擬合。並回答以下問題"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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NIHC/bIuPFWqppfj5fTPvWWSdNsMbmW4TRn53tXoEHcbqItdRLHBC+Z47LBc2Cz5N+ELB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\nXC6dtLT9RfGwBoLo3EkD1rr0Qcztulf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      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"720\"\n",
       "            height=\"480\"\n",
       "            src=\"https://www.youtube.com/embed/fegAeph9UaA\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x7f8223d3fe10>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo(\"fegAeph9UaA\", width=720, height=480)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 模型的泛化能力 (generalization) 是指什麼？ "
   ]
  },
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   "source": [
    "ANS：指訓練好的模型，對於在新的數據上表現是否與在原先數據上的表現一致"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 分類問題與回歸問題分別可用的目標函數有哪些？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ANS：分類問題可用 Accuracy、Cross Entropy等；回歸問題可用 Mean Square Error、Mean Absolute Error等"
   ]
  },
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