msb-public
/
ML-For-Beginners
mirror of https://github.com/microsoft/ML-For-Beginners.git
1
0
Code Issues Projects Releases Wiki Activity
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from 'main'
${ noResults }
ML-For-Beginners/translations/tw/4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb

650 lines
24 KiB
Raw Permalink Blame History Escape

This file contains ambiguous Unicode characters!

This file contains ambiguous Unicode characters that may be confused with others in your current locale. If your use case is intentional and legitimate, you can safely ignore this warning. Use the Escape button to highlight these characters.

{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "lesson_12-R.ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "ir",
"display_name": "R"
},
"language_info": {
"name": "R"
},
"coopTranslator": {
"original_hash": "fab50046ca413a38939d579f8432274f",
"translation_date": "2025-09-03T20:33:25+00:00",
"source_file": "4-Classification/3-Classifiers-2/solution/R/lesson_12-R.ipynb",
"language_code": "tw"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "jsFutf_ygqSx"
},
"source": [
"# 建立分類模型：美味的亞洲和印度料理\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。我們還將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",
"### **前置條件**\n",
"\n",
"我們假設您已完成之前的課程，因為我們將延續之前學到的一些概念。\n",
"\n",
"在本課程中，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",
"您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，以下腳本會檢查您是否已安裝完成此模組所需的套件，並在缺少時為您安裝。\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，我們試圖解答一個問題：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項。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您的估算器（另一個分類器的術語）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：這是一套一致且靈活的 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，我們了解到在不同的菜系中有一組常見的成分。此外，菜系的數量分佈也非常不均。\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": [
"### 處理不平衡數據\n",
"\n",
"不平衡數據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",
"處理不平衡數據集主要有兩種方法：\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。在我們的案例中，我們希望在 `訓練集` 中的菜系數量分佈是均等的。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",
"在上一節課中，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，試圖找到一個*超平面*來「最佳」地分隔不同的類別。我們來看一個簡單的例子：\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，但僅有小的間距。H3~ 則以最大的間距分隔類別。\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）家族中的一種機器學習技術。在 SVC 中，超平面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 對異常值的抵抗力更強，因此對新數據的泛化能力更好。調節這種違規的參數被稱為`cost`，其默認值為 1（請參閱 `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*工作流程*中，接下來可以進行線性 SVC 的訓練，並在此過程中評估結果。關於性能指標，我們將建立一組指標來評估：`準確率`、`敏感度`、`正確預測值`以及`F 值`。\n",
"\n",
"> `augment()` 會將預測結果作為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，旨在適應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。SVM 使用的一種流行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": [
"太棒了 🤩！\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）是一種算法，其中每個觀測值根據其與其他觀測值的*相似性*進行預測。\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": [
"看起來這個模型的表現不是很好。可能透過更改模型的參數（請參考 `help(\"nearest_neighbor\")`）可以提升模型的表現。務必嘗試一下。\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",
"> 來深入了解 *K*-最近鄰分類器。\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`：構建一系列模型，彼此之間相互改進以提升預測性能。\n",
"\n",
"我們先從嘗試隨機森林模型開始。隨機森林模型會構建大量的決策樹，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 Boosted Tree 模型。\n",
"\n",
"Boosted Tree 定義了一種集成方法，通過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",
"有多種方式可以擬合此模型（請參閱 `help(\"boost_tree\")`）。在這個例子中，我們將通過 `xgboost` 引擎來擬合 Boosted Tree。\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 的一個良好替代方案。\n",
">\n",
"> 以了解更多關於集成分類器的內容。\n",
"\n",
"## 4. 額外部分 - 比較多個模型\n",
"\n",
"在這次實驗中，我們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) 函數允許您用更簡潔且更易讀的代碼替代許多 for 迴圈。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/) 套件讓使用者能夠建立並輕鬆擬合大量模型，但主要設計是用於像 `交叉驗證` 這類的重抽樣技術，我們尚未涵蓋這部分。\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對模型品質可能產生的影響。\n",
"\n",
"若想了解特定模型及其參數，可使用：`help(\"model\")`，例如 `help(\"rand_forest\")`\n",
"\n",
"> 在實際操作中，我們通常透過在 `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**。\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[這份術語表](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott)，幫助你理解重要的概念！\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 更加親切且吸引人。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",
"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 創作</figcaption>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n---\n\n**免責聲明**： \n本文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。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。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而產生的任何誤解或錯誤解讀概不負責。\n"
]
}
]
}
Reference in new issue View Git Blame Copy Permalink