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ML-For-Beginners/translations/hk/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb

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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "jsFutf_ygqSx"
},
"source": [
"# 建立分類模型：美味的亞洲和印度美食\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HD54bEefgtNO"
},
"source": [
"## 美食分類器 2\n",
"\n",
"在這第二部分的分類課程中，我們將探索`更多方法`來分類類別型數據。我們還會了解選擇不同分類器所帶來的影響。\n",
"\n",
"### [**課前測驗**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/)\n",
"\n",
"### **先決條件**\n",
"\n",
"我們假設你已完成之前的課程，因為我們將延續之前學到的一些概念。\n",
"\n",
"在這節課中，我們需要以下套件：\n",
"\n",
"- `tidyverse`： [tidyverse](https://www.tidyverse.org/) 是一個[由 R 套件組成的集合](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/) 提供額外的配方步驟，用於處理不平衡數據。\n",
"\n",
"你可以使用以下指令安裝它們：\n",
"\n",
"`install.packages(c(\"tidyverse\", \"tidymodels\", \"kernlab\", \"themis\", \"ranger\", \"xgboost\", \"kknn\"))`\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，並在缺少時為你安裝。\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "vZ57IuUxgyQt"
},
"source": [
"suppressWarnings(if (!require(\"pacman\"))install.packages(\"pacman\"))\n",
"\n",
"pacman::p_load(tidyverse, tidymodels, themis, kernlab, ranger, xgboost, kknn)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "z22M-pj4g07x"
},
"source": [
"## **1. 分類地圖**\n",
"\n",
"在我們的[上一課](https://github.com/microsoft/ML-For-Beginners/tree/main/4-Classification/2-Classifiers-1)中，我們嘗試解答一個問題：如何在多個模型之間進行選擇？在很大程度上，這取決於數據的特性以及我們想要解決的問題類型（例如分類或回歸）。\n",
"\n",
"之前，我們學習了使用 Microsoft 的速查表進行數據分類的各種選擇。Python 的機器學習框架 Scikit-learn 提供了一個類似但更細緻的速查表，可以進一步幫助縮小估算器（另一個分類器的術語）的範圍：\n",
"\n",
"<p >\n",
" <img src=\"../../images/map.png\"\n",
" width=\"700\"/>\n",
" <figcaption></figcaption>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "u1i3xRIVg7vG"
},
"source": [
"> 提示：[在線查看此地圖](https://scikit-learn.org/stable/tutorial/machine_learning_map/)，並沿著路徑點擊以閱讀相關文檔。\n",
">\n",
"> [Tidymodels 參考網站](https://www.tidymodels.org/find/parsnip/#models)也提供了關於不同模型類型的出色文檔。\n",
"\n",
"### **計劃** 🗺️\n",
"\n",
"當你對數據有清晰的理解時，這張地圖非常有幫助，因為你可以沿著它的路徑“走”到一個決策：\n",
"\n",
"- 我們有超過 50 個樣本\n",
"\n",
"- 我們想要預測一個類別\n",
"\n",
"- 我們有標籤數據\n",
"\n",
"- 我們的樣本少於 100K\n",
"\n",
"- ✨ 我們可以選擇 Linear SVC\n",
"\n",
"- 如果這不起作用，因為我們有數值型數據\n",
"\n",
" - 我們可以嘗試 ✨ KNeighbors Classifier\n",
"\n",
" - 如果這不起作用，嘗試 ✨ SVC 和 ✨ Ensemble Classifiers\n",
"\n",
"這是一條非常有幫助的路徑。現在，讓我們使用 [tidymodels](https://www.tidymodels.org/) 建模框架直接開始吧：這是一個一致且靈活的 R 套件集合，旨在鼓勵良好的統計實踐 😊。\n",
"\n",
"## 2. 分割數據並處理不平衡數據集\n",
"\n",
"在之前的課程中，我們了解到不同菜系之間有一組常見的成分。此外，菜系的數量分佈也非常不均衡。\n",
"\n",
"我們將通過以下方式處理這些問題：\n",
"\n",
"- 使用 `dplyr::select()` 刪除那些在不同菜系之間造成混淆的最常見成分。\n",
"\n",
"- 使用一個 `recipe` 預處理數據，通過應用 `over-sampling` 算法使其準備好進行建模。\n",
"\n",
"我們在之前的課程中已經看過上述內容，所以這應該會非常輕鬆 🥳！\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "6tj_rN00hClA"
},
"source": [
"# Load the core Tidyverse and Tidymodels packages\n",
"library(tidyverse)\n",
"library(tidymodels)\n",
"\n",
"# Load the original cuisines data\n",
"df <- read_csv(file = \"https://raw.githubusercontent.com/microsoft/ML-For-Beginners/main/4-Classification/data/cuisines.csv\")\n",
"\n",
"# Drop id column, rice, garlic and ginger from our original data set\n",
"df_select <- df %>% \n",
" select(-c(1, rice, garlic, ginger)) %>%\n",
" # Encode cuisine column as categorical\n",
" mutate(cuisine = factor(cuisine))\n",
"\n",
"\n",
"# Create data split specification\n",
"set.seed(2056)\n",
"cuisines_split <- initial_split(data = df_select,\n",
" strata = cuisine,\n",
" prop = 0.7)\n",
"\n",
"# Extract the data in each split\n",
"cuisines_train <- training(cuisines_split)\n",
"cuisines_test <- testing(cuisines_split)\n",
"\n",
"# Display distribution of cuisines in the training set\n",
"cuisines_train %>% \n",
" count(cuisine) %>% \n",
" arrange(desc(n))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "zFin5yw3hHb1"
},
"source": [
"### 處理數據不平衡\n",
"\n",
"數據不平衡通常會對模型性能產生負面影響。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",
"處理數據不平衡主要有兩種方法：\n",
"\n",
"- 增加少數類別的觀測數量：`過採樣`，例如使用 SMOTE 演算法，該演算法通過少數類別案例的最近鄰居合成生成新的少數類別樣本。\n",
"\n",
"- 移除多數類別的觀測數量：`欠採樣`\n",
"\n",
"在之前的課程中，我們展示了如何使用 `recipe` 處理數據不平衡。`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。在我們的案例中，我們希望在 `訓練集` 中實現菜系數量的均等分佈。現在就讓我們開始吧！\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "cRzTnHolhLWd"
},
"source": [
"# Load themis package for dealing with imbalanced data\n",
"library(themis)\n",
"\n",
"# Create a recipe for preprocessing training data\n",
"cuisines_recipe <- recipe(cuisine ~ ., data = cuisines_train) %>%\n",
" step_smote(cuisine) \n",
"\n",
"# Print recipe\n",
"cuisines_recipe"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "KxOQ2ORhhO81"
},
"source": [
"現在我們準備好訓練模型了 👩‍💻👨‍💻！\n",
"\n",
"## 3. 超越多項式迴歸模型\n",
"\n",
"在上一課中，我們探討了多項式迴歸模型。現在讓我們來看看一些更靈活的分類模型。\n",
"\n",
"### 支援向量機\n",
"\n",
"在分類的背景下，`支援向量機`是一種機器學習技術，旨在尋找一個*超平面*，以「最佳」方式分隔不同的類別。讓我們看一個簡單的例子：\n",
"\n",
"<p >\n",
" <img src=\"../../images/svm.png\"\n",
" width=\"300\"/>\n",
" <figcaption>https://commons.wikimedia.org/w/index.php?curid=22877598</figcaption>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "C4Wsd0vZhXYu"
},
"source": [
"H1~ 不會分隔類別。H2~ 會分隔，但只有很小的間距。H3~ 則會以最大的間距分隔。\n",
"\n",
"#### 線性支持向量分類器\n",
"\n",
"支持向量聚類（SVC）是支持向量機（SVM）這個機器學習技術家族中的一員。在 SVC 中，超平面會被選擇用來正確分隔`大部分`的訓練觀測值，但`可能會錯誤分類`一些觀測值。通過允許某些點位於錯誤的一側，SVM 對異常值的容忍度更高，因此對新數據的泛化能力更強。調節這種違規程度的參數稱為`cost`，其默認值為 1（參見 `help(\"svm_poly\")`）。\n",
"\n",
"讓我們通過在多項式 SVM 模型中設置 `degree = 1` 來創建一個線性 SVC。\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "vJpp6nuChlBz"
},
"source": [
"# Make a linear SVC specification\n",
"svc_linear_spec <- svm_poly(degree = 1) %>% \n",
" set_engine(\"kernlab\") %>% \n",
" set_mode(\"classification\")\n",
"\n",
"# Bundle specification and recipe into a worklow\n",
"svc_linear_wf <- workflow() %>% \n",
" add_recipe(cuisines_recipe) %>% \n",
" add_model(svc_linear_spec)\n",
"\n",
"# Print out workflow\n",
"svc_linear_wf"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "rDs8cWNkhoqu"
},
"source": [
"現在我們已經將預處理步驟和模型規範整合到一個*工作流程*中，接下來可以進行線性SVC的訓練並同時評估結果。至於性能指標，我們可以建立一個指標集來評估：`準確率`、`敏感度`、`正向預測值`以及`F值`\n",
"\n",
"> `augment()` 會將預測結果的欄位新增到給定的數據中。\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "81wiqcwuhrnq"
},
"source": [
"# Train a linear SVC model\n",
"svc_linear_fit <- svc_linear_wf %>% \n",
" fit(data = cuisines_train)\n",
"\n",
"# Create a metric set\n",
"eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
"\n",
"\n",
"# Make predictions and Evaluate model performance\n",
"svc_linear_fit %>% \n",
" augment(new_data = cuisines_test) %>% \n",
" eval_metrics(truth = cuisine, estimate = .pred_class)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "0UFQvHf-huo3"
},
"source": [
"#### 支援向量機\n",
"\n",
"支援向量機（SVM）是支援向量分類器的延伸，旨在處理類別之間的非線性邊界。本質上，SVM 使用*核技巧*來擴展特徵空間，以適應類別之間的非線性關係。一種受歡迎且非常靈活的核函數是*徑向基函數*。讓我們看看它在我們的數據上會有怎樣的表現。\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "-KX4S8mzhzmp"
},
"source": [
"set.seed(2056)\n",
"\n",
"# Make an RBF SVM specification\n",
"svm_rbf_spec <- svm_rbf() %>% \n",
" set_engine(\"kernlab\") %>% \n",
" set_mode(\"classification\")\n",
"\n",
"# Bundle specification and recipe into a worklow\n",
"svm_rbf_wf <- workflow() %>% \n",
" add_recipe(cuisines_recipe) %>% \n",
" add_model(svm_rbf_spec)\n",
"\n",
"\n",
"# Train an RBF model\n",
"svm_rbf_fit <- svm_rbf_wf %>% \n",
" fit(data = cuisines_train)\n",
"\n",
"\n",
"# Make predictions and Evaluate model performance\n",
"svm_rbf_fit %>% \n",
" augment(new_data = cuisines_test) %>% \n",
" eval_metrics(truth = cuisine, estimate = .pred_class)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "QBFSa7WSh4HQ"
},
"source": [
"好得多 🤩！\n",
"\n",
"> ✅ 請參閱：\n",
">\n",
"> - [*支持向量機*](https://bradleyboehmke.github.io/HOML/svm.html)，《Hands-on Machine Learning with R》\n",
">\n",
"> - [*支持向量機*](https://www.statlearning.com/)，《An Introduction to Statistical Learning with Applications in R》\n",
">\n",
"> 以獲取更多閱讀資料。\n",
"\n",
"### 最近鄰分類器\n",
"\n",
"*K*-最近鄰（KNN）是一種基於每個觀測值與其他觀測值之間的*相似性*來進行預測的算法。\n",
"\n",
"讓我們將其應用到我們的數據中。\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "k4BxxBcdh9Ka"
},
"source": [
"# Make a KNN specification\n",
"knn_spec <- nearest_neighbor() %>% \n",
" set_engine(\"kknn\") %>% \n",
" set_mode(\"classification\")\n",
"\n",
"# Bundle recipe and model specification into a workflow\n",
"knn_wf <- workflow() %>% \n",
" add_recipe(cuisines_recipe) %>% \n",
" add_model(knn_spec)\n",
"\n",
"# Train a boosted tree model\n",
"knn_wf_fit <- knn_wf %>% \n",
" fit(data = cuisines_train)\n",
"\n",
"\n",
"# Make predictions and Evaluate model performance\n",
"knn_wf_fit %>% \n",
" augment(new_data = cuisines_test) %>% \n",
" eval_metrics(truth = cuisine, estimate = .pred_class)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "HaegQseriAcj"
},
"source": [
"看來這個模型的表現不太理想。可能透過更改模型的參數（請參考 `help(\"nearest_neighbor\")`）可以改善模型的表現。記得試試看。\n",
"\n",
"> ✅ 請參考：\n",
">\n",
"> - [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/)\n",
">\n",
"> - [An Introduction to Statistical Learning with Applications in R](https://www.statlearning.com/)\n",
">\n",
"> 了解更多關於 *K*-最近鄰分類器的資訊。\n",
"\n",
"### 集成分類器\n",
"\n",
"集成算法透過結合多個基礎估算器來生成最佳模型，其方法包括：\n",
"\n",
"`bagging`：對一組基礎模型應用*平均函數*\n",
"\n",
"`boosting`：建立一系列模型，彼此之間相互改進以提升預測性能。\n",
"\n",
"我們先試試隨機森林模型，它會建立大量的決策樹，然後應用平均函數以生成更好的整體模型。\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "49DPoVs6iK1M"
},
"source": [
"# Make a random forest specification\n",
"rf_spec <- rand_forest() %>% \n",
" set_engine(\"ranger\") %>% \n",
" set_mode(\"classification\")\n",
"\n",
"# Bundle recipe and model specification into a workflow\n",
"rf_wf <- workflow() %>% \n",
" add_recipe(cuisines_recipe) %>% \n",
" add_model(rf_spec)\n",
"\n",
"# Train a random forest model\n",
"rf_wf_fit <- rf_wf %>% \n",
" fit(data = cuisines_train)\n",
"\n",
"\n",
"# Make predictions and Evaluate model performance\n",
"rf_wf_fit %>% \n",
" augment(new_data = cuisines_test) %>% \n",
" eval_metrics(truth = cuisine, estimate = .pred_class)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "RGVYwC_aiUWc"
},
"source": [
"做得好 👏！\n",
"\n",
"我們也來嘗試使用提升樹模型。\n",
"\n",
"提升樹是一種集成方法，它建立一系列連續的決策樹，每棵樹都依賴於前一棵樹的結果，試圖逐步減少錯誤。它專注於那些被錯誤分類的項目的權重，並調整下一個分類器的擬合以進行修正。\n",
"\n",
"有多種方法可以擬合此模型（請參閱 `help(\"boost_tree\")`）。在這個例子中，我們將通過 `xgboost` 引擎來擬合提升樹。\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Py1YWo-micWs"
},
"source": [
"# Make a boosted tree specification\n",
"boost_spec <- boost_tree(trees = 200) %>% \n",
" set_engine(\"xgboost\") %>% \n",
" set_mode(\"classification\")\n",
"\n",
"# Bundle recipe and model specification into a workflow\n",
"boost_wf <- workflow() %>% \n",
" add_recipe(cuisines_recipe) %>% \n",
" add_model(boost_spec)\n",
"\n",
"# Train a boosted tree model\n",
"boost_wf_fit <- boost_wf %>% \n",
" fit(data = cuisines_train)\n",
"\n",
"\n",
"# Make predictions and Evaluate model performance\n",
"boost_wf_fit %>% \n",
" augment(new_data = cuisines_test) %>% \n",
" eval_metrics(truth = cuisine, estimate = .pred_class)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "zNQnbuejigZM"
},
"source": [
"> ✅ 請參閱：\n",
">\n",
"> - [社會科學家的機器學習](https://cimentadaj.github.io/ml_socsci/tree-based-methods.html#random-forests)\n",
">\n",
"> - [R 的實作機器學習](https://bradleyboehmke.github.io/HOML/)\n",
">\n",
"> - [統計學習入門：R 的應用](https://www.statlearning.com/)\n",
">\n",
"> - <https://algotech.netlify.app/blog/xgboost/> - 探討 AdaBoost 模型，它是 xgboost 的一個不錯替代方案。\n",
">\n",
"> 了解更多有關集成分類器的資訊。\n",
"\n",
"## 4. 額外部分 - 比較多個模型\n",
"\n",
"我們在這次實驗中已經擬合了相當多的模型 🙌。如果需要從不同的預處理器和/或模型規範中建立大量工作流程，然後逐一計算性能指標，這可能會變得繁瑣或費力。\n",
"\n",
"讓我們看看是否可以通過創建一個函數來解決這個問題。該函數可以在訓練集上擬合一系列工作流程，然後根據測試集返回性能指標。我們將使用 [purrr](https://purrr.tidyverse.org/) 套件中的 `map()` 和 `map_dfr()` 來對列表中的每個元素應用函數。\n",
"\n",
"> [`map()`](https://purrr.tidyverse.org/reference/map.html) 函數可以讓你用更簡潔且易於閱讀的代碼替代許多 for 循環。學習 [`map()`](https://purrr.tidyverse.org/reference/map.html) 函數的最佳地方是 R for Data Science 中的 [迭代章節](http://r4ds.had.co.nz/iteration.html)。\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Qzb7LyZnimd2"
},
"source": [
"set.seed(2056)\n",
"\n",
"# Create a metric set\n",
"eval_metrics <- metric_set(ppv, sens, accuracy, f_meas)\n",
"\n",
"# Define a function that returns performance metrics\n",
"compare_models <- function(workflow_list, train_set, test_set){\n",
" \n",
" suppressWarnings(\n",
" # Fit each model to the train_set\n",
" map(workflow_list, fit, data = train_set) %>% \n",
" # Make predictions on the test set\n",
" map_dfr(augment, new_data = test_set, .id = \"model\") %>%\n",
" # Select desired columns\n",
" select(model, cuisine, .pred_class) %>% \n",
" # Evaluate model performance\n",
" group_by(model) %>% \n",
" eval_metrics(truth = cuisine, estimate = .pred_class) %>% \n",
" ungroup()\n",
" )\n",
" \n",
"} # End of function"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Fwa712sNisDA"
},
"source": []
},
{
"cell_type": "code",
"metadata": {
"id": "3i4VJOi2iu-a"
},
"source": [
"# Make a list of workflows\n",
"workflow_list <- list(\n",
" \"svc\" = svc_linear_wf,\n",
" \"svm\" = svm_rbf_wf,\n",
" \"knn\" = knn_wf,\n",
" \"random_forest\" = rf_wf,\n",
" \"xgboost\" = boost_wf)\n",
"\n",
"# Call the function\n",
"set.seed(2056)\n",
"perf_metrics <- compare_models(workflow_list = workflow_list, train_set = cuisines_train, test_set = cuisines_test)\n",
"\n",
"# Print out performance metrics\n",
"perf_metrics %>% \n",
" group_by(.metric) %>% \n",
" arrange(desc(.estimate)) %>% \n",
" slice_head(n=7)\n",
"\n",
"# Compare accuracy\n",
"perf_metrics %>% \n",
" filter(.metric == \"accuracy\") %>% \n",
" arrange(desc(.estimate))\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "KuWK_lEli4nW"
},
"source": [
"[**workflowset**](https://workflowsets.tidymodels.org/) 套件讓用戶可以建立並輕鬆擬合大量模型，但主要是為了配合像是 `cross-validation` 這類的重抽樣技術使用，而這部分我們尚未涵蓋。\n",
"\n",
"## **🚀挑戰**\n",
"\n",
"每種技術都有許多參數可以調整，例如 SVM 的 `cost`、KNN 的 `neighbors`、隨機森林的 `mtry`（隨機選擇的預測變數）。\n",
"\n",
"研究每種模型的預設參數，並思考調整這些參數會對模型的品質產生什麼影響。\n",
"\n",
"若想了解更多關於特定模型及其參數的資訊，可以使用：`help(\"model\")`，例如 `help(\"rand_forest\")`\n",
"\n",
"> 實際上，我們通常會透過在 `模擬數據集` 上訓練多個模型並測量這些模型的表現來*估算*這些參數的*最佳值*。這個過程稱為 **調參（tuning）**。\n",
"\n",
"### [**課後測驗**](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/)\n",
"\n",
"### **複習與自學**\n",
"\n",
"這些課程中有許多術語，因此花點時間查看[這份清單](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott)，了解一些實用的術語吧！\n",
"\n",
"#### 特別感謝：\n",
"\n",
"[`Allison Horst`](https://twitter.com/allison_horst/) 創作了這些令人驚嘆的插圖，讓 R 語言變得更親切、更有趣。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",
"祝學習愉快，\n",
"\n",
"[Eric](https://twitter.com/ericntay)，Gold Microsoft Learn 學生大使。\n",
"\n",
"<p >\n",
" <img src=\"../../images/r_learners_sm.jpeg\"\n",
" width=\"569\"/>\n",
" <figcaption>插圖由 @allison_horst 提供</figcaption>\n"
]
},
{
"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，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"
]
}
]
}
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