ML-For-Beginners/translations/hk/4-Classification/2-Classifiers-1/solution/R/lesson_11-R.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# 建立分類模型：美味的亞洲和印度美食\n"
   ],
   "metadata": {
    "id": "zs2woWv_HoE8"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 美食分類器 1\n",
    "\n",
    "在這節課中，我們將探索多種分類器，*根據一組食材來預測特定的國家美食。* 同時，我們也會學習如何利用算法進行分類任務。\n",
    "\n",
    "### [**課前測驗**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/21/)\n",
    "\n",
    "### **準備工作**\n",
    "\n",
    "這節課是基於我們的[上一節課](https://github.com/microsoft/ML-For-Beginners/blob/main/4-Classification/1-Introduction/solution/lesson_10-R.ipynb)，在那節課中我們：\n",
    "\n",
    "-   使用關於亞洲和印度各種美食的數據集進行了簡單的分類介紹 😋。\n",
    "\n",
    "-   探索了一些 [dplyr 動詞](https://dplyr.tidyverse.org/) 來準備和清理數據。\n",
    "\n",
    "-   使用 ggplot2 創建了漂亮的可視化圖表。\n",
    "\n",
    "-   演示了如何通過使用 [recipes](https://recipes.tidymodels.org/articles/Simple_Example.html) 預處理不平衡數據。\n",
    "\n",
    "-   演示了如何 `prep` 和 `bake` 我們的 recipe，以確認它能如預期運作。\n",
    "\n",
    "#### **先決條件**\n",
    "\n",
    "在這節課中，我們需要以下套件來清理、準備和可視化數據：\n",
    "\n",
    "-   `tidyverse`: [tidyverse](https://www.tidyverse.org/) 是一個 [R 套件集合](https://www.tidyverse.org/packages)，旨在讓數據科學更快速、更簡單、更有趣！\n",
    "\n",
    "-   `tidymodels`: [tidymodels](https://www.tidymodels.org/) 框架是一個 [套件集合](https://www.tidymodels.org/packages)，用於建模和機器學習。\n",
    "\n",
    "-   `themis`: [themis 套件](https://themis.tidymodels.org/) 提供了額外的 Recipes 步驟，用於處理不平衡數據。\n",
    "\n",
    "-   `nnet`: [nnet 套件](https://cran.r-project.org/web/packages/nnet/nnet.pdf) 提供了估算具有單一隱藏層的前饋神經網絡以及多項式邏輯回歸模型的功能。\n",
    "\n",
    "你可以通過以下方式安裝它們：\n"
   ],
   "metadata": {
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   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "`install.packages(c(\"tidyverse\", \"tidymodels\", \"DataExplorer\", \"here\"))`\n",
    "\n",
    "或者，以下腳本會檢查你是否已安裝完成此模組所需的套件，若未安裝，則會自動為你安裝。\n"
   ],
   "metadata": {
    "id": "4V85BGCjII7F"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\r\n",
    "\r\n",
    "pacman::p_load(tidyverse, tidymodels, themis, here)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Loading required package: pacman\n",
      "\n"
     ]
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "an5NPyyKIKNR",
    "outputId": "834d5e74-f4b8-49f9-8ab5-4c52ff2d7bc8"
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  {
   "cell_type": "markdown",
   "source": [
    "## 1. 將數據分成訓練集和測試集\n",
    "\n",
    "我們先從上一課的一些步驟開始。\n",
    "\n",
    "### 使用 `dplyr::select()` 移除最常見的食材，這些食材容易在不同菜系之間造成混淆。\n",
    "\n",
    "人人都愛米飯、大蒜和薑！\n"
   ],
   "metadata": {
    "id": "0ax9GQLBINVv"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "# Load the original cuisines data\r\n",
    "df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\r\n",
    "\r\n",
    "# Drop id column, rice, garlic and ginger from our original data set\r\n",
    "df_select <- df %>% \r\n",
    "  select(-c(1, rice, garlic, ginger)) %>%\r\n",
    "  # Encode cuisine column as categorical\r\n",
    "  mutate(cuisine = factor(cuisine))\r\n",
    "\r\n",
    "# Display new data set\r\n",
    "df_select %>% \r\n",
    "  slice_head(n = 5)\r\n",
    "\r\n",
    "# Display distribution of cuisines\r\n",
    "df_select %>% \r\n",
    "  count(cuisine) %>% \r\n",
    "  arrange(desc(n))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "New names:\n",
      "* `` -> ...1\n",
      "\n",
      "\u001b[1m\u001b[1mRows: \u001b[1m\u001b[22m\u001b[34m\u001b[34m2448\u001b[34m\u001b[39m \u001b[1m\u001b[1mColumns: \u001b[1m\u001b[22m\u001b[34m\u001b[34m385\u001b[34m\u001b[39m\n",
      "\n",
      "\u001b[36m──\u001b[39m \u001b[1m\u001b[1mColumn specification\u001b[1m\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\n",
      "\u001b[1mDelimiter:\u001b[22m \",\"\n",
      "\u001b[31mchr\u001b[39m   (1): cuisine\n",
      "\u001b[32mdbl\u001b[39m (384): ...1, almond, angelica, anise, anise_seed, apple, apple_brandy, a...\n",
      "\n",
      "\n",
      "\u001b[36mℹ\u001b[39m Use \u001b[30m\u001b[47m\u001b[30m\u001b[47m`spec()`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to retrieve the full column specification for this data.\n",
      "\u001b[36mℹ\u001b[39m Specify the column types or set \u001b[30m\u001b[47m\u001b[30m\u001b[47m`show_col_types = FALSE`\u001b[47m\u001b[30m\u001b[49m\u001b[39m to quiet this message.\n",
      "\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
       "1 indian  0      0        0     0          0     0            0       0       \n",
       "2 indian  1      0        0     0          0     0            0       0       \n",
       "3 indian  0      0        0     0          0     0            0       0       \n",
       "4 indian  0      0        0     0          0     0            0       0       \n",
       "5 indian  0      0        0     0          0     0            0       0       \n",
       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
       "1 0         ⋯ 0       0           0          0                       0    0   \n",
       "2 0         ⋯ 0       0           0          0                       0    0   \n",
       "3 0         ⋯ 0       0           0          0                       0    0   \n",
       "4 0         ⋯ 0       0           0          0                       0    0   \n",
       "5 0         ⋯ 0       0           0          0                       0    0   \n",
       "  yam yeast yogurt zucchini\n",
       "1 0   0     0      0       \n",
       "2 0   0     0      0       \n",
       "3 0   0     0      0       \n",
       "4 0   0     0      0       \n",
       "5 0   0     1      0       "
      ],
      "text/markdown": [
       "\n",
       "A tibble: 5 × 381\n",
       "\n",
       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| indian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |\n",
       "\n"
      ],
      "text/latex": [
       "A tibble: 5 × 381\n",
       "\\begin{tabular}{lllllllllllllllllllll}\n",
       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\t indian & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\t indian & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 5 × 381</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>indian</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>indian</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "  cuisine  n  \n",
       "1 korean   799\n",
       "2 indian   598\n",
       "3 chinese  442\n",
       "4 japanese 320\n",
       "5 thai     289"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 5 × 2\n",
       "\n",
       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
       "|---|---|\n",
       "| korean   | 799 |\n",
       "| indian   | 598 |\n",
       "| chinese  | 442 |\n",
       "| japanese | 320 |\n",
       "| thai     | 289 |\n",
       "\n"
      ],
      "text/latex": [
       "A tibble: 5 × 2\n",
       "\\begin{tabular}{ll}\n",
       " cuisine & n\\\\\n",
       " <fct> & <int>\\\\\n",
       "\\hline\n",
       "\t korean   & 799\\\\\n",
       "\t indian   & 598\\\\\n",
       "\t chinese  & 442\\\\\n",
       "\t japanese & 320\\\\\n",
       "\t thai     & 289\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 5 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>korean  </td><td>799</td></tr>\n",
       "\t<tr><td>indian  </td><td>598</td></tr>\n",
       "\t<tr><td>chinese </td><td>442</td></tr>\n",
       "\t<tr><td>japanese</td><td>320</td></tr>\n",
       "\t<tr><td>thai    </td><td>289</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 735
    },
    "id": "jhCrrH22IWVR",
    "outputId": "d444a85c-1d8b-485f-bc4f-8be2e8f8217c"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "太好了！現在是時候將數據分割成70%用於訓練，30%用於測試。我們還會在分割數據時應用「分層」技術，以「保持每種菜系的比例」在訓練和驗證數據集中。\n",
    "\n",
    "[rsample](https://rsample.tidymodels.org/) 是 Tidymodels 中的一個套件，提供高效數據分割和重抽樣的基礎設施：\n"
   ],
   "metadata": {
    "id": "AYTjVyajIdny"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# Load the core Tidymodels packages into R session\r\n",
    "library(tidymodels)\r\n",
    "\r\n",
    "# Create split specification\r\n",
    "set.seed(2056)\r\n",
    "cuisines_split <- initial_split(data = df_select,\r\n",
    "                                strata = cuisine,\r\n",
    "                                prop = 0.7)\r\n",
    "\r\n",
    "# Extract the data in each split\r\n",
    "cuisines_train <- training(cuisines_split)\r\n",
    "cuisines_test <- testing(cuisines_split)\r\n",
    "\r\n",
    "# Print the number of cases in each split\r\n",
    "cat(\"Training cases: \", nrow(cuisines_train), \"\\n\",\r\n",
    "    \"Test cases: \", nrow(cuisines_test), sep = \"\")\r\n",
    "\r\n",
    "# Display the first few rows of the training set\r\n",
    "cuisines_train %>% \r\n",
    "  slice_head(n = 5)\r\n",
    "\r\n",
    "\r\n",
    "# Display distribution of cuisines in the training set\r\n",
    "cuisines_train %>% \r\n",
    "  count(cuisine) %>% \r\n",
    "  arrange(desc(n))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training cases: 1712\n",
      "Test cases: 736"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "  cuisine almond angelica anise anise_seed apple apple_brandy apricot armagnac\n",
       "1 chinese 0      0        0     0          0     0            0       0       \n",
       "2 chinese 0      0        0     0          0     0            0       0       \n",
       "3 chinese 0      0        0     0          0     0            0       0       \n",
       "4 chinese 0      0        0     0          0     0            0       0       \n",
       "5 chinese 0      0        0     0          0     0            0       0       \n",
       "  artemisia ⋯ whiskey white_bread white_wine whole_grain_wheat_flour wine wood\n",
       "1 0         ⋯ 0       0           0          0                       1    0   \n",
       "2 0         ⋯ 0       0           0          0                       1    0   \n",
       "3 0         ⋯ 0       0           0          0                       0    0   \n",
       "4 0         ⋯ 0       0           0          0                       0    0   \n",
       "5 0         ⋯ 0       0           0          0                       0    0   \n",
       "  yam yeast yogurt zucchini\n",
       "1 0   0     0      0       \n",
       "2 0   0     0      0       \n",
       "3 0   0     0      0       \n",
       "4 0   0     0      0       \n",
       "5 0   0     0      0       "
      ],
      "text/markdown": [
       "\n",
       "A tibble: 5 × 381\n",
       "\n",
       "| cuisine &lt;fct&gt; | almond &lt;dbl&gt; | angelica &lt;dbl&gt; | anise &lt;dbl&gt; | anise_seed &lt;dbl&gt; | apple &lt;dbl&gt; | apple_brandy &lt;dbl&gt; | apricot &lt;dbl&gt; | armagnac &lt;dbl&gt; | artemisia &lt;dbl&gt; | ⋯ ⋯ | whiskey &lt;dbl&gt; | white_bread &lt;dbl&gt; | white_wine &lt;dbl&gt; | whole_grain_wheat_flour &lt;dbl&gt; | wine &lt;dbl&gt; | wood &lt;dbl&gt; | yam &lt;dbl&gt; | yeast &lt;dbl&gt; | yogurt &lt;dbl&gt; | zucchini &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 |\n",
       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "| chinese | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |\n",
       "\n"
      ],
      "text/latex": [
       "A tibble: 5 × 381\n",
       "\\begin{tabular}{lllllllllllllllllllll}\n",
       " cuisine & almond & angelica & anise & anise\\_seed & apple & apple\\_brandy & apricot & armagnac & artemisia & ⋯ & whiskey & white\\_bread & white\\_wine & whole\\_grain\\_wheat\\_flour & wine & wood & yam & yeast & yogurt & zucchini\\\\\n",
       " <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\t chinese & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 5 × 381</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>cuisine</th><th scope=col>almond</th><th scope=col>angelica</th><th scope=col>anise</th><th scope=col>anise_seed</th><th scope=col>apple</th><th scope=col>apple_brandy</th><th scope=col>apricot</th><th scope=col>armagnac</th><th scope=col>artemisia</th><th scope=col>⋯</th><th scope=col>whiskey</th><th scope=col>white_bread</th><th scope=col>white_wine</th><th scope=col>whole_grain_wheat_flour</th><th scope=col>wine</th><th scope=col>wood</th><th scope=col>yam</th><th scope=col>yeast</th><th scope=col>yogurt</th><th scope=col>zucchini</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>chinese</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ]
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "  cuisine  n  \n",
       "1 korean   559\n",
       "2 indian   418\n",
       "3 chinese  309\n",
       "4 japanese 224\n",
       "5 thai     202"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 5 × 2\n",
       "\n",
       "| cuisine &lt;fct&gt; | n &lt;int&gt; |\n",
       "|---|---|\n",
       "| korean   | 559 |\n",
       "| indian   | 418 |\n",
       "| chinese  | 309 |\n",
       "| japanese | 224 |\n",
       "| thai     | 202 |\n",
       "\n"
      ],
      "text/latex": [
       "A tibble: 5 × 2\n",
       "\\begin{tabular}{ll}\n",
       " cuisine & n\\\\\n",
       " <fct> & <int>\\\\\n",
       "\\hline\n",
       "\t korean   & 559\\\\\n",
       "\t indian   & 418\\\\\n",
       "\t chinese  & 309\\\\\n",
       "\t japanese & 224\\\\\n",
       "\t thai     & 202\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 5 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>cuisine</th><th scope=col>n</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>korean  </td><td>559</td></tr>\n",
       "\t<tr><td>indian  </td><td>418</td></tr>\n",
       "\t<tr><td>chinese </td><td>309</td></tr>\n",
       "\t<tr><td>japanese</td><td>224</td></tr>\n",
       "\t<tr><td>thai    </td><td>202</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 535
    },
    "id": "w5FWIkEiIjdN",
    "outputId": "2e195fd9-1a8f-4b91-9573-cce5582242df"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 2. 處理不平衡數據\n",
    "\n",
    "正如你可能已經注意到，在原始數據集以及我們的訓練集裡，菜式的數量分佈非常不均衡。韓國菜的數量幾乎是泰國菜的三倍。不平衡數據通常會對模型的表現產生負面影響。許多模型在觀測數量相等時表現最佳，因此在面對不平衡數據時往往會遇到困難。\n",
    "\n",
    "處理不平衡數據集主要有兩種方法：\n",
    "\n",
    "-   為少數類別添加觀測值：`過採樣`，例如使用 SMOTE 演算法，該演算法通過使用這些案例的最近鄰居合成生成少數類別的新樣本。\n",
    "\n",
    "-   從多數類別中移除觀測值：`欠採樣`\n",
    "\n",
    "在之前的課程中，我們演示了如何使用 `recipe` 來處理不平衡數據集。`recipe` 可以被視為一種藍圖，描述了應該對數據集應用哪些步驟以使其準備好進行數據分析。在我們的情況下，我們希望在 `訓練集` 中實現菜式數量的均衡分佈。讓我們直接開始吧！\n"
   ],
   "metadata": {
    "id": "daBi9qJNIwqW"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "# Load themis package for dealing with imbalanced data\r\n",
    "library(themis)\r\n",
    "\r\n",
    "# Create a recipe for preprocessing training data\r\n",
    "cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>% \r\n",
    "  step_smote(cuisine)\r\n",
    "\r\n",
    "# Print recipe\r\n",
    "cuisines_recipe"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Data Recipe\n",
       "\n",
       "Inputs:\n",
       "\n",
       "      role #variables\n",
       "   outcome          1\n",
       " predictor        380\n",
       "\n",
       "Operations:\n",
       "\n",
       "SMOTE based on cuisine"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 200
    },
    "id": "Az6LFBGxI1X0",
    "outputId": "29d71d85-64b0-4e62-871e-bcd5398573b6"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "你當然可以先確認（使用 prep 和 bake）這個食譜是否如你所期望地運作——所有標籤為 `559` 的菜式觀察數據。\n",
    "\n",
    "由於我們將使用這個食譜作為建模的預處理器，一個 `workflow()` 會幫我們完成所有的準備和烘焙工作，因此我們不需要手動估算這個食譜。\n",
    "\n",
    "現在我們準備好訓練模型了 👩‍💻👨‍💻！\n",
    "\n",
    "## 3. 選擇你的分類器\n",
    "\n",
    "<p >\n",
    "   <img src=\"../../images/parsnip.jpg\"\n",
    "   width=\"600\"/>\n",
    "   <figcaption>插畫由 @allison_horst 提供</figcaption>\n"
   ],
   "metadata": {
    "id": "NBL3PqIWJBBB"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "現在我們需要決定使用哪種算法來完成這項工作 🤔。\n",
    "\n",
    "在 Tidymodels 中，[`parsnip package`](https://parsnip.tidymodels.org/index.html) 提供了一個一致的介面，用於在不同的引擎（套件）中使用模型。請參閱 parsnip 的文檔以探索[模型類型與引擎](https://www.tidymodels.org/find/parsnip/#models)及其對應的[模型參數](https://www.tidymodels.org/find/parsnip/#model-args)。一開始看起來種類繁多，令人眼花繚亂。例如，以下方法都包含分類技術：\n",
    "\n",
    "-   C5.0 規則型分類模型\n",
    "\n",
    "-   靈活判別模型\n",
    "\n",
    "-   線性判別模型\n",
    "\n",
    "-   正則化判別模型\n",
    "\n",
    "-   邏輯回歸模型\n",
    "\n",
    "-   多項式回歸模型\n",
    "\n",
    "-   樸素貝葉斯模型\n",
    "\n",
    "-   支援向量機\n",
    "\n",
    "-   最近鄰居法\n",
    "\n",
    "-   決策樹\n",
    "\n",
    "-   集成方法\n",
    "\n",
    "-   神經網絡\n",
    "\n",
    "這個列表還可以繼續延伸！\n",
    "\n",
    "### **選擇哪個分類器？**\n",
    "\n",
    "那麼，應該選擇哪個分類器呢？通常，嘗試多種方法並尋找效果良好的結果是一種測試方式。\n",
    "\n",
    "> AutoML 可以巧妙地解決這個問題，通過在雲端運行這些比較，幫助您選擇最適合您數據的算法。試試看 [這裡](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-77952-leestott)\n",
    "\n",
    "此外，分類器的選擇取決於我們的問題。例如，當結果可以分為`多於兩個類別`時，就像我們的情況一樣，您必須使用`多類別分類算法`而不是`二元分類算法`。\n",
    "\n",
    "### **更好的方法**\n",
    "\n",
    "比隨意猜測更好的方法是參考這份可下載的[機器學習備忘單](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-77952-leestott)。在這裡，我們發現，針對我們的多類別問題，我們有一些選擇：\n",
    "\n",
    "<p >\n",
    "   <img src=\"../../images/cheatsheet.png\"\n",
    "   width=\"500\"/>\n",
    "   <figcaption>微軟算法備忘單的一部分，詳細介紹了多類別分類選項</figcaption>\n"
   ],
   "metadata": {
    "id": "a6DLAZ3vJZ14"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### **推理**\n",
    "\n",
    "讓我們嘗試根據現有的限制來推敲不同的解決方法：\n",
    "\n",
    "-   **深度神經網絡太過繁重**。考慮到我們的數據集雖然乾淨但規模有限，以及我們是在本地筆記本中進行訓練，深度神經網絡對於這項任務來說過於繁重。\n",
    "\n",
    "-   **不使用二分類器**。我們不使用二分類器，因此排除了 one-vs-all 的方法。\n",
    "\n",
    "-   **決策樹或邏輯回歸可能有效**。決策樹可能有效，或者對於多分類數據可以使用多項式回歸/多分類邏輯回歸。\n",
    "\n",
    "-   **多分類提升決策樹解決的是不同的問題**。多分類提升決策樹最適合非參數任務，例如設計用於建立排名的任務，因此對我們來說並不適用。\n",
    "\n",
    "此外，通常在嘗試更複雜的機器學習模型（例如集成方法）之前，先建立最簡單的模型來了解情況是個好主意。因此，在這節課中，我們將從 `多項式回歸` 模型開始。\n",
    "\n",
    "> 邏輯回歸是一種用於結果變量是分類（或名義）時的技術。對於二元邏輯回歸，結果變量的數量是兩個，而對於多項式邏輯回歸，結果變量的數量超過兩個。請參閱 [進階回歸方法](https://bookdown.org/chua/ber642_advanced_regression/multinomial-logistic-regression.html) 以了解更多。\n",
    "\n",
    "## 4. 訓練並評估多項式邏輯回歸模型\n",
    "\n",
    "在 Tidymodels 中，`parsnip::multinom_reg()` 定義了一個使用線性預測器來通過多項分佈預測多分類數據的模型。請參閱 `?multinom_reg()` 了解可以用來擬合此模型的不同方法/引擎。\n",
    "\n",
    "在這個例子中，我們將通過默認的 [nnet](https://cran.r-project.org/web/packages/nnet/nnet.pdf) 引擎來擬合多項式回歸模型。\n",
    "\n",
    "> 我隨機選擇了一個 `penalty` 的值。選擇此值有更好的方法，例如使用 `重抽樣` 和 `調整` 模型，我們稍後會討論。\n",
    ">\n",
    "> 如果您想了解更多有關如何調整模型超參數的內容，請參閱 [Tidymodels: Get Started](https://www.tidymodels.org/start/tuning/)。\n"
   ],
   "metadata": {
    "id": "gWMsVcbBJemu"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "# Create a multinomial regression model specification\r\n",
    "mr_spec <- multinom_reg(penalty = 1) %>% \r\n",
    "  set_engine(\"nnet\", MaxNWts = 2086) %>% \r\n",
    "  set_mode(\"classification\")\r\n",
    "\r\n",
    "# Print model specification\r\n",
    "mr_spec"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "Multinomial Regression Model Specification (classification)\n",
       "\n",
       "Main Arguments:\n",
       "  penalty = 1\n",
       "\n",
       "Engine-Specific Arguments:\n",
       "  MaxNWts = 2086\n",
       "\n",
       "Computational engine: nnet \n"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 166
    },
    "id": "Wq_fcyQiJvfG",
    "outputId": "c30449c7-3864-4be7-f810-72a003743e2d"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "做得好 🥳！現在我們已經有了一個食譜和模型規格，我們需要找到一種方法將它們整合成一個物件，這個物件首先會對數據進行預處理，然後在預處理後的數據上擬合模型，並且還能支持潛在的後處理活動。在 Tidymodels 中，這個方便的物件叫做 [`workflow`](https://workflows.tidymodels.org/)，它能方便地保存你的建模組件！這就像我們在 *Python* 中所說的 *pipelines*。\n",
    "\n",
    "那麼，讓我們把所有東西整合到一個 workflow 中吧！📦\n"
   ],
   "metadata": {
    "id": "NlSbzDfgJ0zh"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# Bundle recipe and model specification\r\n",
    "mr_wf <- workflow() %>% \r\n",
    "  add_recipe(cuisines_recipe) %>% \r\n",
    "  add_model(mr_spec)\r\n",
    "\r\n",
    "# Print out workflow\r\n",
    "mr_wf"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "══ Workflow ════════════════════════════════════════════════════════════════════\n",
       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
       "\n",
       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
       "1 Recipe Step\n",
       "\n",
       "• step_smote()\n",
       "\n",
       "── Model ───────────────────────────────────────────────────────────────────────\n",
       "Multinomial Regression Model Specification (classification)\n",
       "\n",
       "Main Arguments:\n",
       "  penalty = 1\n",
       "\n",
       "Engine-Specific Arguments:\n",
       "  MaxNWts = 2086\n",
       "\n",
       "Computational engine: nnet \n"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 333
    },
    "id": "Sc1TfPA4Ke3_",
    "outputId": "82c70013-e431-4e7e-cef6-9fcf8aad4a6c"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "工作流程 👌👌！**`workflow()`** 可以像模型一樣進行擬合。所以，是時候訓練模型了！\n"
   ],
   "metadata": {
    "id": "TNQ8i85aKf9L"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "# Train a multinomial regression model\n",
    "mr_fit <- fit(object = mr_wf, data = cuisines_train)\n",
    "\n",
    "mr_fit"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "══ Workflow [trained] ══════════════════════════════════════════════════════════\n",
       "\u001b[3mPreprocessor:\u001b[23m Recipe\n",
       "\u001b[3mModel:\u001b[23m multinom_reg()\n",
       "\n",
       "── Preprocessor ────────────────────────────────────────────────────────────────\n",
       "1 Recipe Step\n",
       "\n",
       "• step_smote()\n",
       "\n",
       "── Model ───────────────────────────────────────────────────────────────────────\n",
       "Call:\n",
       "nnet::multinom(formula = ..y ~ ., data = data, decay = ~1, MaxNWts = ~2086, \n",
       "    trace = FALSE)\n",
       "\n",
       "Coefficients:\n",
       "         (Intercept)     almond angelica         anise anise_seed       apple\n",
       "indian    0.19723325  0.2409661        0 -5.004955e-05 -0.1657635 -0.05769734\n",
       "japanese  0.13961959 -0.6262400        0 -1.169155e-04 -0.4893596 -0.08585717\n",
       "korean    0.22377347 -0.1833485        0 -5.560395e-05 -0.2489401 -0.15657804\n",
       "thai     -0.04336577 -0.6106258        0  4.903828e-04 -0.5782866  0.63451105\n",
       "         apple_brandy     apricot armagnac   artemisia artichoke   asparagus\n",
       "indian              0  0.37042636        0 -0.09122797         0 -0.27181970\n",
       "japanese            0  0.28895643        0 -0.12651100         0  0.14054037\n",
       "korean              0 -0.07981259        0  0.55756709         0 -0.66979948\n",
       "thai                0 -0.33160904        0 -0.10725182         0 -0.02602152\n",
       "             avocado       bacon baked_potato balm     banana     barley\n",
       "indian   -0.46624197  0.16008055            0    0 -0.2838796  0.2230625\n",
       "japanese  0.90341344  0.02932727            0    0 -0.4142787  2.0953906\n",
       "korean   -0.06925382 -0.35804134            0    0 -0.2686963 -0.7233404\n",
       "thai     -0.21473955 -0.75594439            0    0  0.6784880 -0.4363320\n",
       "         bartlett_pear      basil        bay       bean         beech\n",
       "indian               0 -0.7128756  0.1011587 -0.8777275 -0.0004380795\n",
       "japanese             0  0.1288697  0.9425626 -0.2380748  0.3373437611\n",
       "korean               0 -0.2445193 -0.4744318 -0.8957870 -0.0048784496\n",
       "thai                 0  1.5365848  0.1333256  0.2196970 -0.0113078024\n",
       "               beef beef_broth   beef_liver         beer        beet\n",
       "indian   -0.7985278  0.2430186 -0.035598065 -0.002173738  0.01005813\n",
       "japanese  0.2241875 -0.3653020 -0.139551027  0.128905553  0.04923911\n",
       "korean    0.5366515 -0.6153237  0.213455197 -0.010828645  0.27325423\n",
       "thai      0.1570012 -0.9364154 -0.008032213 -0.035063746 -0.28279823\n",
       "         bell_pepper bergamot       berry bitter_orange black_bean\n",
       "indian    0.49074330        0  0.58947607   0.191256164 -0.1945233\n",
       "japanese  0.09074167        0 -0.25917977  -0.118915977 -0.3442400\n",
       "korean   -0.57876763        0 -0.07874180  -0.007729435 -0.5220672\n",
       "thai      0.92554006        0 -0.07210196  -0.002983296 -0.4614426\n",
       "         black_currant black_mustard_seed_oil black_pepper black_raspberry\n",
       "indian               0             0.38935801   -0.4453495               0\n",
       "japanese             0            -0.05452887   -0.5440869               0\n",
       "korean               0            -0.03929970    0.8025454               0\n",
       "thai                 0            -0.21498372   -0.9854806               0\n",
       "         black_sesame_seed  black_tea   blackberry blackberry_brandy\n",
       "indian          -0.2759246  0.3079977  0.191256164                 0\n",
       "japanese        -0.6101687 -0.1671913 -0.118915977                 0\n",
       "korean           1.5197674 -0.3036261 -0.007729435                 0\n",
       "thai            -0.1755656 -0.1487033 -0.002983296                 0\n",
       "         blue_cheese    blueberry   bone_oil bourbon_whiskey      brandy\n",
       "indian             0  0.216164294 -0.2276744               0  0.22427587\n",
       "japanese           0 -0.119186087  0.3913019               0 -0.15595599\n",
       "korean             0 -0.007821986  0.2854487               0 -0.02562342\n",
       "thai               0 -0.004947048 -0.0253658               0 -0.05715244\n",
       "\n",
       "...\n",
       "and 308 more lines."
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "GMbdfVmTKkJI",
    "outputId": "adf9ebdf-d69d-4a64-e9fd-e06e5322292e"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "模型訓練期間學到的係數已顯示。\n",
    "\n",
    "### 評估訓練好的模型\n",
    "\n",
    "現在是時候透過測試集來評估模型的表現 📏！首先，讓我們在測試集上進行預測。\n"
   ],
   "metadata": {
    "id": "tt2BfOxrKmcJ"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "# Make predictions on the test set\n",
    "results <- cuisines_test %>% select(cuisine) %>% \n",
    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test))\n",
    "\n",
    "# Print out results\n",
    "results %>% \n",
    "  slice_head(n = 5)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "  cuisine .pred_class\n",
       "1 indian  thai       \n",
       "2 indian  indian     \n",
       "3 indian  indian     \n",
       "4 indian  indian     \n",
       "5 indian  indian     "
      ],
      "text/markdown": [
       "\n",
       "A tibble: 5 × 2\n",
       "\n",
       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; |\n",
       "|---|---|\n",
       "| indian | thai   |\n",
       "| indian | indian |\n",
       "| indian | indian |\n",
       "| indian | indian |\n",
       "| indian | indian |\n",
       "\n"
      ],
      "text/latex": [
       "A tibble: 5 × 2\n",
       "\\begin{tabular}{ll}\n",
       " cuisine & .pred\\_class\\\\\n",
       " <fct> & <fct>\\\\\n",
       "\\hline\n",
       "\t indian & thai  \\\\\n",
       "\t indian & indian\\\\\n",
       "\t indian & indian\\\\\n",
       "\t indian & indian\\\\\n",
       "\t indian & indian\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 5 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>indian</td><td>thai  </td></tr>\n",
       "\t<tr><td>indian</td><td>indian</td></tr>\n",
       "\t<tr><td>indian</td><td>indian</td></tr>\n",
       "\t<tr><td>indian</td><td>indian</td></tr>\n",
       "\t<tr><td>indian</td><td>indian</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 248
    },
    "id": "CqtckvtsKqax",
    "outputId": "e57fe557-6a68-4217-fe82-173328c5436d"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "出色的工作！在 Tidymodels 中，可以使用 [yardstick](https://yardstick.tidymodels.org/) 評估模型性能——這是一個用於通過性能指標衡量模型效果的套件。正如我們在邏輯回歸課程中所做的那樣，讓我們從計算混淆矩陣開始。\n"
   ],
   "metadata": {
    "id": "8w5N6XsBKss7"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "# Confusion matrix for categorical data\n",
    "conf_mat(data = results, truth = cuisine, estimate = .pred_class)\n"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "          Truth\n",
       "Prediction chinese indian japanese korean thai\n",
       "  chinese       83      1        8     15   10\n",
       "  indian         4    163        1      2    6\n",
       "  japanese      21      5       73     25    1\n",
       "  korean        15      0       11    191    0\n",
       "  thai          10     11        3      7   70"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 133
    },
    "id": "YvODvsLkK0iG",
    "outputId": "bb69da84-1266-47ad-b174-d43b88ca2988"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "當處理多個類別時，通常更直觀的方式是將其視為熱圖，如下所示：\n"
   ],
   "metadata": {
    "id": "c0HfPL16Lr6U"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "update_geom_defaults(geom = \"tile\", new = list(color = \"black\", alpha = 0.7))\n",
    "# Visualize confusion matrix\n",
    "results %>% \n",
    "  conf_mat(cuisine, .pred_class) %>% \n",
    "  autoplot(type = \"heatmap\")"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "plot without title"
      ],
      "image/png": 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"
     },
     "metadata": {
      "image/png": {
       "width": 420,
       "height": 420
      }
     }
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 436
    },
    "id": "HsAtwukyLsvt",
    "outputId": "3032a224-a2c8-4270-b4f2-7bb620317400"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "混淆矩陣圖中較深的方格表示案例數量較多，希望你可以看到一條深色方格的對角線，代表預測標籤與實際標籤一致的案例。\n",
    "\n",
    "現在讓我們計算混淆矩陣的摘要統計數據。\n"
   ],
   "metadata": {
    "id": "oOJC87dkLwPr"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "# Summary stats for confusion matrix\n",
    "conf_mat(data = results, truth = cuisine, estimate = .pred_class) %>% \n",
    "summary()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "   .metric              .estimator .estimate\n",
       "1  accuracy             multiclass 0.7880435\n",
       "2  kap                  multiclass 0.7276583\n",
       "3  sens                 macro      0.7780927\n",
       "4  spec                 macro      0.9477598\n",
       "5  ppv                  macro      0.7585583\n",
       "6  npv                  macro      0.9460080\n",
       "7  mcc                  multiclass 0.7292724\n",
       "8  j_index              macro      0.7258524\n",
       "9  bal_accuracy         macro      0.8629262\n",
       "10 detection_prevalence macro      0.2000000\n",
       "11 precision            macro      0.7585583\n",
       "12 recall               macro      0.7780927\n",
       "13 f_meas               macro      0.7641862"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 13 × 3\n",
       "\n",
       "| .metric &lt;chr&gt; | .estimator &lt;chr&gt; | .estimate &lt;dbl&gt; |\n",
       "|---|---|---|\n",
       "| accuracy             | multiclass | 0.7880435 |\n",
       "| kap                  | multiclass | 0.7276583 |\n",
       "| sens                 | macro      | 0.7780927 |\n",
       "| spec                 | macro      | 0.9477598 |\n",
       "| ppv                  | macro      | 0.7585583 |\n",
       "| npv                  | macro      | 0.9460080 |\n",
       "| mcc                  | multiclass | 0.7292724 |\n",
       "| j_index              | macro      | 0.7258524 |\n",
       "| bal_accuracy         | macro      | 0.8629262 |\n",
       "| detection_prevalence | macro      | 0.2000000 |\n",
       "| precision            | macro      | 0.7585583 |\n",
       "| recall               | macro      | 0.7780927 |\n",
       "| f_meas               | macro      | 0.7641862 |\n",
       "\n"
      ],
      "text/latex": [
       "A tibble: 13 × 3\n",
       "\\begin{tabular}{lll}\n",
       " .metric & .estimator & .estimate\\\\\n",
       " <chr> & <chr> & <dbl>\\\\\n",
       "\\hline\n",
       "\t accuracy             & multiclass & 0.7880435\\\\\n",
       "\t kap                  & multiclass & 0.7276583\\\\\n",
       "\t sens                 & macro      & 0.7780927\\\\\n",
       "\t spec                 & macro      & 0.9477598\\\\\n",
       "\t ppv                  & macro      & 0.7585583\\\\\n",
       "\t npv                  & macro      & 0.9460080\\\\\n",
       "\t mcc                  & multiclass & 0.7292724\\\\\n",
       "\t j\\_index              & macro      & 0.7258524\\\\\n",
       "\t bal\\_accuracy         & macro      & 0.8629262\\\\\n",
       "\t detection\\_prevalence & macro      & 0.2000000\\\\\n",
       "\t precision            & macro      & 0.7585583\\\\\n",
       "\t recall               & macro      & 0.7780927\\\\\n",
       "\t f\\_meas               & macro      & 0.7641862\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 13 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>.metric</th><th scope=col>.estimator</th><th scope=col>.estimate</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>accuracy            </td><td>multiclass</td><td>0.7880435</td></tr>\n",
       "\t<tr><td>kap                 </td><td>multiclass</td><td>0.7276583</td></tr>\n",
       "\t<tr><td>sens                </td><td>macro     </td><td>0.7780927</td></tr>\n",
       "\t<tr><td>spec                </td><td>macro     </td><td>0.9477598</td></tr>\n",
       "\t<tr><td>ppv                 </td><td>macro     </td><td>0.7585583</td></tr>\n",
       "\t<tr><td>npv                 </td><td>macro     </td><td>0.9460080</td></tr>\n",
       "\t<tr><td>mcc                 </td><td>multiclass</td><td>0.7292724</td></tr>\n",
       "\t<tr><td>j_index             </td><td>macro     </td><td>0.7258524</td></tr>\n",
       "\t<tr><td>bal_accuracy        </td><td>macro     </td><td>0.8629262</td></tr>\n",
       "\t<tr><td>detection_prevalence</td><td>macro     </td><td>0.2000000</td></tr>\n",
       "\t<tr><td>precision           </td><td>macro     </td><td>0.7585583</td></tr>\n",
       "\t<tr><td>recall              </td><td>macro     </td><td>0.7780927</td></tr>\n",
       "\t<tr><td>f_meas              </td><td>macro     </td><td>0.7641862</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 494
    },
    "id": "OYqetUyzL5Wz",
    "outputId": "6a84d65e-113d-4281-dfc1-16e8b70f37e6"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "如果我們聚焦於一些指標，例如準確性、敏感度、ppv，作為起步其實表現不算差 🥳！\n",
    "\n",
    "## 4. 更深入探討\n",
    "\n",
    "讓我們問一個微妙的問題：究竟是什麼標準用來決定某種菜式作為預測結果？\n",
    "\n",
    "其實，統計機器學習算法，例如邏輯回歸，是基於「概率」的；因此分類器真正預測的是一組可能結果的概率分佈。然後，具有最高概率的類別會被選為給定觀察的最可能結果。\n",
    "\n",
    "讓我們通過進行硬分類預測和概率來看看這個過程的實際運作。\n"
   ],
   "metadata": {
    "id": "43t7vz8vMJtW"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "# Make hard class prediction and probabilities\n",
    "results_prob <- cuisines_test %>%\n",
    "  select(cuisine) %>% \n",
    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test)) %>% \n",
    "  bind_cols(mr_fit %>% predict(new_data = cuisines_test, type = \"prob\"))\n",
    "\n",
    "# Print out results\n",
    "results_prob %>% \n",
    "  slice_head(n = 5)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "  cuisine .pred_class .pred_chinese .pred_indian .pred_japanese .pred_korean\n",
       "1 indian  thai        1.551259e-03  0.4587877    5.988039e-04   2.428503e-04\n",
       "2 indian  indian      2.637133e-05  0.9999488    6.648651e-07   2.259993e-05\n",
       "3 indian  indian      1.049433e-03  0.9909982    1.060937e-03   1.644947e-05\n",
       "4 indian  indian      6.237482e-02  0.4763035    9.136702e-02   3.660913e-01\n",
       "5 indian  indian      1.431745e-02  0.9418551    2.945239e-02   8.721782e-03\n",
       "  .pred_thai  \n",
       "1 5.388194e-01\n",
       "2 1.577948e-06\n",
       "3 6.874989e-03\n",
       "4 3.863391e-03\n",
       "5 5.653283e-03"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 5 × 7\n",
       "\n",
       "| cuisine &lt;fct&gt; | .pred_class &lt;fct&gt; | .pred_chinese &lt;dbl&gt; | .pred_indian &lt;dbl&gt; | .pred_japanese &lt;dbl&gt; | .pred_korean &lt;dbl&gt; | .pred_thai &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "| indian | thai   | 1.551259e-03 | 0.4587877 | 5.988039e-04 | 2.428503e-04 | 5.388194e-01 |\n",
       "| indian | indian | 2.637133e-05 | 0.9999488 | 6.648651e-07 | 2.259993e-05 | 1.577948e-06 |\n",
       "| indian | indian | 1.049433e-03 | 0.9909982 | 1.060937e-03 | 1.644947e-05 | 6.874989e-03 |\n",
       "| indian | indian | 6.237482e-02 | 0.4763035 | 9.136702e-02 | 3.660913e-01 | 3.863391e-03 |\n",
       "| indian | indian | 1.431745e-02 | 0.9418551 | 2.945239e-02 | 8.721782e-03 | 5.653283e-03 |\n",
       "\n"
      ],
      "text/latex": [
       "A tibble: 5 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " cuisine & .pred\\_class & .pred\\_chinese & .pred\\_indian & .pred\\_japanese & .pred\\_korean & .pred\\_thai\\\\\n",
       " <fct> & <fct> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t indian & thai   & 1.551259e-03 & 0.4587877 & 5.988039e-04 & 2.428503e-04 & 5.388194e-01\\\\\n",
       "\t indian & indian & 2.637133e-05 & 0.9999488 & 6.648651e-07 & 2.259993e-05 & 1.577948e-06\\\\\n",
       "\t indian & indian & 1.049433e-03 & 0.9909982 & 1.060937e-03 & 1.644947e-05 & 6.874989e-03\\\\\n",
       "\t indian & indian & 6.237482e-02 & 0.4763035 & 9.136702e-02 & 3.660913e-01 & 3.863391e-03\\\\\n",
       "\t indian & indian & 1.431745e-02 & 0.9418551 & 2.945239e-02 & 8.721782e-03 & 5.653283e-03\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 5 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>cuisine</th><th scope=col>.pred_class</th><th scope=col>.pred_chinese</th><th scope=col>.pred_indian</th><th scope=col>.pred_japanese</th><th scope=col>.pred_korean</th><th scope=col>.pred_thai</th></tr>\n",
       "\t<tr><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>indian</td><td>thai  </td><td>1.551259e-03</td><td>0.4587877</td><td>5.988039e-04</td><td>2.428503e-04</td><td>5.388194e-01</td></tr>\n",
       "\t<tr><td>indian</td><td>indian</td><td>2.637133e-05</td><td>0.9999488</td><td>6.648651e-07</td><td>2.259993e-05</td><td>1.577948e-06</td></tr>\n",
       "\t<tr><td>indian</td><td>indian</td><td>1.049433e-03</td><td>0.9909982</td><td>1.060937e-03</td><td>1.644947e-05</td><td>6.874989e-03</td></tr>\n",
       "\t<tr><td>indian</td><td>indian</td><td>6.237482e-02</td><td>0.4763035</td><td>9.136702e-02</td><td>3.660913e-01</td><td>3.863391e-03</td></tr>\n",
       "\t<tr><td>indian</td><td>indian</td><td>1.431745e-02</td><td>0.9418551</td><td>2.945239e-02</td><td>8.721782e-03</td><td>5.653283e-03</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 248
    },
    "id": "xdKNs-ZPMTJL",
    "outputId": "68f6ac5a-725a-4eff-9ea6-481fef00e008"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "✅ 可以解釋為什麼模型很確定第一個觀察是泰國菜嗎？\n",
    "\n",
    "## **🚀挑戰**\n",
    "\n",
    "在這節課中，你使用清理過的數據建立了一個機器學習模型，能根據一系列食材預測國家菜系。花些時間閱讀 [Tidymodels 提供的多種選項](https://www.tidymodels.org/find/parsnip/#models) 來分類數據，以及 [其他方法](https://parsnip.tidymodels.org/articles/articles/Examples.html#multinom_reg-models) 來進行多項式回歸。\n",
    "\n",
    "#### 特別感謝：\n",
    "\n",
    "[`Allison Horst`](https://twitter.com/allison_horst/) 創作了令人驚嘆的插圖，讓 R 更加友好和吸引人。可以在她的 [畫廊](https://www.google.com/url?q=https://github.com/allisonhorst/stats-illustrations&sa=D&source=editors&ust=1626380772530000&usg=AOvVaw3zcfyCizFQZpkSLzxiiQEM) 找到更多插圖。\n",
    "\n",
    "[Cassie Breviu](https://www.twitter.com/cassieview) 和 [Jen Looper](https://www.twitter.com/jenlooper) 創建了這個模組的原始 Python 版本 ♥️\n",
    "\n",
    "<br>\n",
    "本來想加些笑話，但我對食物的雙關語一點都不懂 😅。\n",
    "\n",
    "<br>\n",
    "\n",
    "祝學習愉快，\n",
    "\n",
    "[Eric](https://twitter.com/ericntay)，Microsoft Learn 金牌學生大使。\n"
   ],
   "metadata": {
    "id": "2tWVHMeLMYdM"
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n---\n\n**免責聲明**：  \n本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始語言的文件應被視為權威來源。對於重要資訊，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。\n"
   ]
  }
 ]
}