From c1945983ef5c0c21c4a8d03abb49b69bc47a57b1 Mon Sep 17 00:00:00 2001 From: XiaojianTang <85986768+XiaojianTang@users.noreply.github.com> Date: Thu, 29 Jul 2021 09:15:48 +0800 Subject: [PATCH 01/12] Simplified Chinese Translation v1 --- .../translations/README.zh-cn.md | 245 ++++++++++++++++++ 1 file changed, 245 insertions(+) create mode 100644 4-Classification/2-Classifiers-1/translations/README.zh-cn.md diff --git a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md new file mode 100644 index 00000000..00327c92 --- /dev/null +++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md @@ -0,0 +1,245 @@ +# 菜品分类器1 + +在本节中，将使用你在上一个课程中所保存的全部经过均衡和清洗的菜品数据。 + +You will use this dataset with a variety of classifiers to _predict a given national cuisine based on a group of ingredients_. While doing so, you'll learn more about some of the ways that algorithms can be leveraged for classification tasks. +你将使用这份数据集，并通过多种分类器 _在给出了各种配料后预测这是那一个国家的菜品_。在此过程中，你将学到更多能够用来调节分类任务算法的方法。 + +## [课前测试](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/) +# 准备工作 + +假设你已经完成了[课程1](../1-Introduction/README.md), 确保在根目录的`/data`文件夹中有 _cleaned_cuisines.csv_ 文件来进行接下来的四节课程。 + +## 练习 - 预测某国的菜品 + +1. 在本节课的 _notebook.ipynb_ 文件中，导入Pandas的同时载入相应的数据文件： + + ```python + import pandas as pd + cuisines_df = pd.read_csv("../../data/cleaned_cuisine.csv") + cuisines_df.head() + ``` + + 数据如下所示: + + ```output + | | Unnamed: 0 | cuisine | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini | + | --- | ---------- | ------- | ------ | -------- | ----- | ---------- | ----- | ------------ | ------- | -------- | --- | ------- | ----------- | ---------- | ----------------------- | ---- | ---- | --- | ----- | ------ | -------- | + | 0 | 0 | indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | + | 1 | 1 | indian | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | + | 2 | 2 | indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | + | 3 | 3 | indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | + | 4 | 4 | indian | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | + ``` + +1. 现在，再多导入一些库： + + ```python + from sklearn.linear_model import LogisticRegression + from sklearn.model_selection import train_test_split, cross_val_score + from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve + from sklearn.svm import SVC + import numpy as np + ``` + +1. 接下来需要将数据分训练模型所需的X和y两个dataframe。首先可将`cuisine`列的数据单独保存为标签（label）的dataframe。 + + ```python + cuisines_label_df = cuisines_df['cuisine'] + cuisines_label_df.head() + ``` + + 输出看上去会是这样: + + ```output + 0 indian + 1 indian + 2 indian + 3 indian + 4 indian + Name: cuisine, dtype: object + ``` + +1. 调用`drop()`函数将 `Unnamed: 0`和 `cuisine`列删除，并将余下的数据作为可以用于训练的特证（feature）: + + ```python + cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1) + cuisines_feature_df.head() + ``` + + 你的特证（feature）数据看上去将会是这样: + + | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini | | + | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | --- | + | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | + | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | + | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | + | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | + | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | + +现在，你已经准备好可以开始训练你的模型了！ + +## 选则你的分类器 + +你的数据已经清洗干净并已经准备好可以进行训练了，现在需要决定你想使用的算法来完成这项任务。 + +Scikit_learn将分类任务归在了监督学习目录中，在这个目录中你将会找到很多方法来进行分类。乍一看上去，有点[琳琅满目](https://scikit-learn.org/stable/supervised_learning.html)。下面这些方法都包含了分类技术： + +- 线性模型（Linear Models） +- 支持向量机（Support Vector Machines） +- 随机梯度下降（Stochastic Gradient Descent） +- 最近邻（Nearest Neighbors） +- 高斯过程（Gaussian Processes） +- 决策树（Decision Trees） +- 集成方法（投票分类器）（Ensemble methods（voting classifier）） +- 多类别多输出算法（多类别多标签分类，多类别多输出分类）（Multiclass and multioutput algorithms (multiclass and multilabel classification, multiclass-multioutput classification)） + +> 你也可以使用[神经网络来分类数据](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#classification), 但这对于本课程来说有点超纲了。 + +### 如何选择分类器? + +那么，你应该选择哪一个分类器呢？一般来说，可以选择多个方法并对比他们运行后的结果。Scikit-learn提供了各种算法（包括KNeighbors、 SVC two ways、 GaussianProcessClassifier、 DecisionTreeClassifier、 RandomForestClassifier、 MLPClassifier、 AdaBoostClassifier、 GaussianNB以及QuadraticDiscrinationAnalysis）的[比较](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html)，并且对比较的结果进行了可视化的展示： + +![各分类器比较](../images/comparison.png) +> 图表来源于Scikit-learn的官方文档 + +> AutoML通过在云端运行这些比较非常完美地解决的这个问题，使得你能够根据你的数据选择最佳的算法。试试[这里](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)。 + +### 一种更好的方法 + +不过，比起无脑地猜测，根据这份可以下载的[机器学习作弊表]中的方法是一个更好的选择。在表中我们可以发现对于这个多类型的分类任务，可以有一些选择： + +A better way than wildly guessing, however, is to follow the ideas on this downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa). Here, we discover that, for our multiclass problem, we have some choices: + +![多类型问题作弊表](../images/cheatsheet.png) +> 微软算法作弊表中关于多类型分类任务可选算法的部分 + +✅ 下载这份作弊表，打印出来，挂在你的墙上吧！ + +### 推导过程 + +Let's see if we can reason our way through different approaches given the constraints we have:让我们看看根据我们所有的限制条件推导下各中方法的可行性： + +- **神经网络（Neural Network）太过复杂了**。我们的数据很清晰但数据量比较小，此外我们是通过notebook在本地进行训练，神经网络对于这个任务来说过于复杂了。 +- **二分类法(two-class classifier)不可行**。我们不能使用二分类法,所以这就排除了一对多（one-vs-all）算法。 +- **决策树以及逻辑回归可行**. 决策树也许有用，或者也可以使用逻辑回归来处理多类型数据。 +- **多类型增强决策数可以解决不同的问题**. 多类型增强决策树最适合非参数化的任务，即任务目标是建立一个排序，这对我们的任务并没有作用。 + +### 使用Scikit-learn + +我们将使用Scikit-learn来分析我们的数据。然而，在Scikit-learn中有很多种方法来使用逻辑回归。可以看一看逻辑回归算法可以[传递的参数](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression)。 + +当我们需要Scikit-learn进行逻辑回归运算时，`multi_class` 以及 `solver`是最重要的两个参数，我们需要特别说明一下哎。 `multi_class` 的值决定了特定的行为。`solver`的值是我们需要使用的算法。并不是所有的solvers都可以匹配`multi_class`的值的。 + +According to the docs, in the multiclass case, the training algorithm根据文档，在多类型问题种，训练的算法: + +- **使用“一对其余”(OvR)策略（scheme）**, 如果`multi_class`被设置为`ovr` +- **使用交叉熵损失（cross entropy loss）**, 如果`multi_class`被设置为`multinomial` (目前，`multinomial`只支持‘lbfgs’, ‘sag’, ‘saga’以及‘newton-cg’等 solver)。 + +> 🎓 其中“scheme”可以是“ovr(one-vs-rest)”也可以是“multinomial”。 因为逻辑回归事实上是设计用于支持二分类任务的，这些scheme将使其可以更好的支持多类型分类任务。[来源](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/) + +> 🎓 “solver”被定义为是"用于解决优化问题的算法"。[来源](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression). + +Scikit-learn提供了以下这个表格来解释solver是如何应对的不同的数据结构所带来的不同的挑战的: + +![solvers](../images/solvers.png) + +## 练习 - 分割数据 + +因为你刚刚在上一节课中学习了逻辑回归，因此我们可以聚焦于此，来演练一下如何进行第一个模型的训练。通过调用`train_test_split()`可以把你的数据分割成训练集和测试集： + + +```python +X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3) +``` + +## 练习 - 应用逻辑回归 + +因为我们正在进行多类型分类的案例，你需要决定选用什么 _scheme_ 以及使用什么 _solver_ 。使用带有multiclass设置的LogisticRegression，并将solver设置为**liblinear**来进行模型训练。 + +1. 创建逻辑回归，并将multi_class设置为`ovr`，同时将solver设置为 `liblinear`: + + ```python + lr = LogisticRegression(multi_class='ovr',solver='liblinear') + model = lr.fit(X_train, np.ravel(y_train)) + + accuracy = model.score(X_test, y_test) + print ("Accuracy is {}".format(accuracy)) + ``` + + ✅ 也可以试试其他solver比如`lbfgs`, 它通常可以作为默认的设置 + + > 注意, 使用Pandas的[`ravel`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ravel.html) 函数可以在需要的时候将你的数据进行降维 + + 准确率高达了**80%**! + +1. 你也可以通过查看一行数据（比如第50行）来观察到模型运行的情况: + + ```python + print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}') + print(f'cuisine: {y_test.iloc[50]}') + ``` + + 运行后的输出如下: + + ```output + ingredients: Index(['cilantro', 'onion', 'pea', 'potato', 'tomato', 'vegetable_oil'], dtype='object') + cuisine: indian + ``` + + ✅ 试试不同的行号来检查一下结果吧 + +1. 让我们再深入研究一下，你可以检查一下这回预测的准确率: + + ```python + test= X_test.iloc[50].values.reshape(-1, 1).T + proba = model.predict_proba(test) + classes = model.classes_ + resultdf = pd.DataFrame(data=proba, columns=classes) + + topPrediction = resultdf.T.sort_values(by=[0], ascending = [False]) + topPrediction.head() + ``` + + 运行后的输出如下———这是一道印度菜的可能性最大，是最合理的猜测: + + | | 0 | | | | | | | | | | | | | | | | | | | | | + | -------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | + | indian | 0.715851 | | | | | | | | | | | | | | | | | | | | | + | chinese | 0.229475 | | | | | | | | | | | | | | | | | | | | | + | japanese | 0.029763 | | | | | | | | | | | | | | | | | | | | | + | korean | 0.017277 | | | | | | | | | | | | | | | | | | | | | + | thai | 0.007634 | | | | | | | | | | | | | | | | | | | | | + + ✅ 你能解释下为什么模型会如此确定这是一道印度菜么？ + +1. 就和你在回归的课程中所做的一样，通过输出分类的报告，我们可以得到更多的细节： + + ```python + y_pred = model.predict(X_test) + print(classification_report(y_test,y_pred)) + ``` + + | precision | recall | f1-score | support | | | | | | | | | | | | | | | | | | | + | ------------ | ------ | -------- | ------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | + | chinese | 0.73 | 0.71 | 0.72 | 229 | | | | | | | | | | | | | | | | | | + | indian | 0.91 | 0.93 | 0.92 | 254 | | | | | | | | | | | | | | | | | | + | japanese | 0.70 | 0.75 | 0.72 | 220 | | | | | | | | | | | | | | | | | | + | korean | 0.86 | 0.76 | 0.81 | 242 | | | | | | | | | | | | | | | | | | + | thai | 0.79 | 0.85 | 0.82 | 254 | | | | | | | | | | | | | | | | | | + | accuracy | 0.80 | 1199 | | | | | | | | | | | | | | | | | | | | + | macro avg | 0.80 | 0.80 | 0.80 | 1199 | | | | | | | | | | | | | | | | | | + | weighted avg | 0.80 | 0.80 | 0.80 | 1199 | | | | | | | | | | | | | | | | | | + +## 挑战 + +在本课程中，你使用了清洗后的数据建立了一个机器学习的模型，能够根据一系列的配料来预测菜品来自于哪个国家。请再花点时间阅读一下Scikit-learn所提供的可以用来分类数据的其他选择。同时也可以深入研究一下“solver”的概念并尝试一下理解其背后的原理。 + +## [课后小测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/) +## 回顾与自学 + +[这个课程](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)将对逻辑回归背后的数学原理进行更加深入的讲解 + +## 作业 + +[学习solver](assignment.md) From ab83692b57b7d9dfec46088388755f697577b165 Mon Sep 17 00:00:00 2001 From: XiaojianTang <85986768+XiaojianTang@users.noreply.github.com> Date: Thu, 29 Jul 2021 09:21:57 +0800 Subject: [PATCH 02/12] Update README.zh-cn.md --- 4-Classification/2-Classifiers-1/translations/README.zh-cn.md | 1 - 1 file changed, 1 deletion(-) diff --git a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md index 00327c92..3386412b 100644 --- a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md +++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md @@ -2,7 +2,6 @@ 在本节中，将使用你在上一个课程中所保存的全部经过均衡和清洗的菜品数据。 -You will use this dataset with a variety of classifiers to _predict a given national cuisine based on a group of ingredients_. While doing so, you'll learn more about some of the ways that algorithms can be leveraged for classification tasks. 你将使用这份数据集，并通过多种分类器 _在给出了各种配料后预测这是那一个国家的菜品_。在此过程中，你将学到更多能够用来调节分类任务算法的方法。 ## [课前测试](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/) From 12c49120a22e386446a57e9f3d80f0224c0e2079 Mon Sep 17 00:00:00 2001 From: XiaojianTang <85986768+XiaojianTang@users.noreply.github.com> Date: Thu, 29 Jul 2021 09:54:53 +0800 Subject: [PATCH 03/12] Correct Typo --- .../translations/README.zh-cn.md | 64 +++++++++---------- 1 file changed, 31 insertions(+), 33 deletions(-) diff --git a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md index 3386412b..2174423a 100644 --- a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md +++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md @@ -1,17 +1,17 @@ # 菜品分类器1 -在本节中，将使用你在上一个课程中所保存的全部经过均衡和清洗的菜品数据。 +本节课程将使用你在上一个课程中所保存的全部经过均衡和清洗的菜品数据。 -你将使用这份数据集，并通过多种分类器 _在给出了各种配料后预测这是那一个国家的菜品_。在此过程中，你将学到更多能够用来调节分类任务算法的方法。 +你将使用这份数据集，并通过多种分类器 _在给出了各种配料后预测这是那一个国家的菜品_。在此过程中，你将学到更多能够用来调试分类任务算法的方法。 ## [课前测试](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/) # 准备工作 -假设你已经完成了[课程1](../1-Introduction/README.md), 确保在根目录的`/data`文件夹中有 _cleaned_cuisines.csv_ 文件来进行接下来的四节课程。 +假设你已经完成了[课程1](../1-Introduction/README.md), 确保在根目录的`/data`文件夹中有 _cleaned_cuisines.csv_ 这份文件来进行接下来的四节课程。 ## 练习 - 预测某国的菜品 -1. 在本节课的 _notebook.ipynb_ 文件中，导入Pandas的同时载入相应的数据文件： +1. 在本节课的 _notebook.ipynb_ 文件中，导入Pandas，并读取相应的数据文件： ```python import pandas as pd @@ -41,14 +41,14 @@ import numpy as np ``` -1. 接下来需要将数据分训练模型所需的X和y两个dataframe。首先可将`cuisine`列的数据单独保存为标签（label）的dataframe。 +1. 接下来需要将数据分为训练模型所需的X（译者注：代表特征数据）和y（译者注：代表标签数据）两个dataframe。首先可将`cuisine`列的数据单独保存为的一个dataframe作为标签（label）。 ```python cuisines_label_df = cuisines_df['cuisine'] cuisines_label_df.head() ``` - 输出看上去会是这样: + 输出如下: ```output 0 indian @@ -59,7 +59,7 @@ Name: cuisine, dtype: object ``` -1. 调用`drop()`函数将 `Unnamed: 0`和 `cuisine`列删除，并将余下的数据作为可以用于训练的特证（feature）: +1. 调用`drop()`函数将 `Unnamed: 0`和 `cuisine`列删除，并将余下的数据作为可以用于训练的特证（feature）数据: ```python cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1) @@ -80,9 +80,9 @@ ## 选则你的分类器 -你的数据已经清洗干净并已经准备好可以进行训练了，现在需要决定你想使用的算法来完成这项任务。 +你的数据已经清洗干净并已经准备好可以进行训练了，现在需要决定你想要使用的算法来完成这项任务。 -Scikit_learn将分类任务归在了监督学习目录中，在这个目录中你将会找到很多方法来进行分类。乍一看上去，有点[琳琅满目](https://scikit-learn.org/stable/supervised_learning.html)。下面这些方法都包含了分类技术： +Scikit_learn将分类任务归在了监督学习类别中，在这个类别中你将可以找到很多可以用来分类的方法。乍一看上去，有点[琳琅满目](https://scikit-learn.org/stable/supervised_learning.html)。以下这些方法都包含了分类技术： - 线性模型（Linear Models） - 支持向量机（Support Vector Machines） @@ -97,45 +97,43 @@ Scikit_learn将分类任务归在了监督学习目录中，在这个目录中 ### 如何选择分类器? -那么，你应该选择哪一个分类器呢？一般来说，可以选择多个方法并对比他们运行后的结果。Scikit-learn提供了各种算法（包括KNeighbors、 SVC two ways、 GaussianProcessClassifier、 DecisionTreeClassifier、 RandomForestClassifier、 MLPClassifier、 AdaBoostClassifier、 GaussianNB以及QuadraticDiscrinationAnalysis）的[比较](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html)，并且对比较的结果进行了可视化的展示： +那么，你应该选择哪一个分类器呢？一般来说，可以多选择几个并对比他们运行后的结果。Scikit-learn提供了各种算法（包括KNeighbors、 SVC two ways、 GaussianProcessClassifier、 DecisionTreeClassifier、 RandomForestClassifier、 MLPClassifier、 AdaBoostClassifier、 GaussianNB以及QuadraticDiscrinationAnalysis）的效果[对比](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html)，并且将比较的结果进行了可视化的展示： ![各分类器比较](../images/comparison.png) > 图表来源于Scikit-learn的官方文档 -> AutoML通过在云端运行这些比较非常完美地解决的这个问题，使得你能够根据你的数据选择最佳的算法。试试[这里](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)。 +> AutoML通过在云端运行这些对比非常完美地解决的选择算法的这个问题，使得你能够根据你的数据特性选择最佳的算法。试试点击[这里](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)了解更多。 -### 一种更好的方法 +### 一种更好的方法来选择分类器 -不过，比起无脑地猜测，根据这份可以下载的[机器学习作弊表]中的方法是一个更好的选择。在表中我们可以发现对于这个多类型的分类任务，可以有一些选择： - -A better way than wildly guessing, however, is to follow the ideas on this downloadable [ML Cheat sheet](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa). Here, we discover that, for our multiclass problem, we have some choices: +不过，比起无脑地猜测，你可以下载这份[机器学习作弊表（cheatsheet）](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa)，对各算法进行对比，这是一个选择算法更有效的办法。在表中我们可以发现对于本课程中涉及的多类型的分类任务，可以有以下这些选择： ![多类型问题作弊表](../images/cheatsheet.png) > 微软算法作弊表中关于多类型分类任务可选算法的部分 ✅ 下载这份作弊表，打印出来，挂在你的墙上吧！ -### 推导过程 +### 选择的过程 -Let's see if we can reason our way through different approaches given the constraints we have:让我们看看根据我们所有的限制条件推导下各中方法的可行性： +让我们看看根据我们所有的限制条件依次判断下各种方法的可行性： - **神经网络（Neural Network）太过复杂了**。我们的数据很清晰但数据量比较小，此外我们是通过notebook在本地进行训练，神经网络对于这个任务来说过于复杂了。 - **二分类法(two-class classifier)不可行**。我们不能使用二分类法,所以这就排除了一对多（one-vs-all）算法。 -- **决策树以及逻辑回归可行**. 决策树也许有用，或者也可以使用逻辑回归来处理多类型数据。 -- **多类型增强决策数可以解决不同的问题**. 多类型增强决策树最适合非参数化的任务，即任务目标是建立一个排序，这对我们的任务并没有作用。 +- **决策树以及逻辑回归可行**。决策树应该是有用的，此外也可以使用逻辑回归来处理多类型数据。 +- **多类型增强决策树是用于解决其他问题的**. 多类型增强决策树最适合非参数化的任务，即任务目标是建立一个排序，这对我们当前的任务并没有作用。 ### 使用Scikit-learn -我们将使用Scikit-learn来分析我们的数据。然而，在Scikit-learn中有很多种方法来使用逻辑回归。可以看一看逻辑回归算法可以[传递的参数](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression)。 +我们将会使用Scikit-learn来对我们的数据进行分析。然而，在Scikit-learn中使用逻辑回归也有很很多方法。可以看一看逻辑回归算法需要[传递的参数](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression)。 -当我们需要Scikit-learn进行逻辑回归运算时，`multi_class` 以及 `solver`是最重要的两个参数，我们需要特别说明一下哎。 `multi_class` 的值决定了特定的行为。`solver`的值是我们需要使用的算法。并不是所有的solvers都可以匹配`multi_class`的值的。 +当我们需要Scikit-learn进行逻辑回归运算时，`multi_class` 以及 `solver`是最重要的两个参数，因此我们需要特别说明一下。 `multi_class` 的值是分类任务要求的某一种特定的行为。`solver`的值是我们需要使用的算法。并不是所有的solvers都可以匹配`multi_class`的值的。 -According to the docs, in the multiclass case, the training algorithm根据文档，在多类型问题种，训练的算法: +根据文档，在多类型问题中，训练的算法应: -- **使用“一对其余”(OvR)策略（scheme）**, 如果`multi_class`被设置为`ovr` -- **使用交叉熵损失（cross entropy loss）**, 如果`multi_class`被设置为`multinomial` (目前，`multinomial`只支持‘lbfgs’, ‘sag’, ‘saga’以及‘newton-cg’等 solver)。 +- **使用“一对其余”(OvR)策略（scheme）**, 当`multi_class`被设置为`ovr`时 +- **使用交叉熵损失（cross entropy loss）**, 当`multi_class`被设置为`multinomial` (目前，`multinomial`只支持‘lbfgs’, ‘sag’, ‘saga’以及‘newton-cg’等 solver)时。 -> 🎓 其中“scheme”可以是“ovr(one-vs-rest)”也可以是“multinomial”。 因为逻辑回归事实上是设计用于支持二分类任务的，这些scheme将使其可以更好的支持多类型分类任务。[来源](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/) +> 🎓 其中“scheme”可以是“ovr(one-vs-rest)”也可以是“multinomial”。 因为逻辑回归本来是设计来用于进行二分类任务的，这两个scheme都可以使得逻辑回归能更好的支持多类型分类任务。[来源](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/) > 🎓 “solver”被定义为是"用于解决优化问题的算法"。[来源](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression). @@ -145,7 +143,7 @@ Scikit-learn提供了以下这个表格来解释solver是如何应对的不同 ## 练习 - 分割数据 -因为你刚刚在上一节课中学习了逻辑回归，因此我们可以聚焦于此，来演练一下如何进行第一个模型的训练。通过调用`train_test_split()`可以把你的数据分割成训练集和测试集： +你刚刚在上一节课中学习了逻辑回归，因此我们可以聚焦于此，来演练一下如何进行第一个模型的训练。首先，需要通过调用`train_test_split()`可以把你的数据分割成训练集和测试集： ```python @@ -154,7 +152,7 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine ## 练习 - 应用逻辑回归 -因为我们正在进行多类型分类的案例，你需要决定选用什么 _scheme_ 以及使用什么 _solver_ 。使用带有multiclass设置的LogisticRegression，并将solver设置为**liblinear**来进行模型训练。 +接着，你需要决定选用什么 _scheme_ 以及 _solver_ 来进行我们这个多类型分类的案例。这里我们使用LogisticRegression方法，并设置相应的multi_class参数，同时将solver设置为**liblinear**来进行模型训练。 1. 创建逻辑回归，并将multi_class设置为`ovr`，同时将solver设置为 `liblinear`: @@ -166,13 +164,13 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine print ("Accuracy is {}".format(accuracy)) ``` - ✅ 也可以试试其他solver比如`lbfgs`, 它通常可以作为默认的设置 + ✅ 也可以试试其他solver比如`lbfgs`, 这通常是默认的设置 > 注意, 使用Pandas的[`ravel`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ravel.html) 函数可以在需要的时候将你的数据进行降维 - 准确率高达了**80%**! + 计算结果准确率高达了**80%**! -1. 你也可以通过查看一行数据（比如第50行）来观察到模型运行的情况: +1. 你也可以通过查看某一行数据（比如第50行）来观察到模型运行的情况: ```python print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}') @@ -186,9 +184,9 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine cuisine: indian ``` - ✅ 试试不同的行号来检查一下结果吧 + ✅ 试试不同的行索引来检查一下结果吧 -1. 让我们再深入研究一下，你可以检查一下这回预测的准确率: +1. 让我们再深入研究一下，你可以检查一下本次预测的准确率: ```python test= X_test.iloc[50].values.reshape(-1, 1).T @@ -200,7 +198,7 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine topPrediction.head() ``` - 运行后的输出如下———这是一道印度菜的可能性最大，是最合理的猜测: + 运行后的输出如下———可以发现这是一道印度菜的可能性最大，是最合理的猜测: | | 0 | | | | | | | | | | | | | | | | | | | | | | -------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | From 83f8a7980b5edfe05c15333e21c7273ccd650b54 Mon Sep 17 00:00:00 2001 From: XiaojianTang <85986768+XiaojianTang@users.noreply.github.com> Date: Fri, 30 Jul 2021 10:46:50 +0800 Subject: [PATCH 04/12] Standardize some terms as other translation file --- .../2-Classifiers-1/translations/README.zh-cn.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md index 2174423a..ac5d80ba 100644 --- a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md +++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md @@ -4,10 +4,10 @@ 你将使用这份数据集，并通过多种分类器 _在给出了各种配料后预测这是那一个国家的菜品_。在此过程中，你将学到更多能够用来调试分类任务算法的方法。 -## [课前测试](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/) +## [课前测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/) # 准备工作 -假设你已经完成了[课程1](../1-Introduction/README.md), 确保在根目录的`/data`文件夹中有 _cleaned_cuisines.csv_ 这份文件来进行接下来的四节课程。 +假如你已经完成了[课程1](../1-Introduction/README.md), 确保在根目录的`/data`文件夹中有 _cleaned_cuisines.csv_ 这份文件来进行接下来的四节课程。 ## 练习 - 预测某国的菜品 @@ -59,14 +59,14 @@ Name: cuisine, dtype: object ``` -1. 调用`drop()`函数将 `Unnamed: 0`和 `cuisine`列删除，并将余下的数据作为可以用于训练的特证（feature）数据: +1. 调用`drop()`方法将 `Unnamed: 0`和 `cuisine`列删除，并将余下的数据作为可以用于训练的特证（feature）数据: ```python cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1) cuisines_feature_df.head() ``` - 你的特证（feature）数据看上去将会是这样: + 你的特征集看上去将会是这样: | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini | | | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | --- | @@ -78,7 +78,7 @@ 现在，你已经准备好可以开始训练你的模型了！ -## 选则你的分类器 +## 选择你的分类器 你的数据已经清洗干净并已经准备好可以进行训练了，现在需要决定你想要使用的算法来完成这项任务。 @@ -232,7 +232,7 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine 在本课程中，你使用了清洗后的数据建立了一个机器学习的模型，能够根据一系列的配料来预测菜品来自于哪个国家。请再花点时间阅读一下Scikit-learn所提供的可以用来分类数据的其他选择。同时也可以深入研究一下“solver”的概念并尝试一下理解其背后的原理。 -## [课后小测](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/) +## [课后测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/) ## 回顾与自学 [这个课程](https://people.eecs.berkeley.edu/~russell/classes/cs194/f11/lectures/CS194%20Fall%202011%20Lecture%2006.pdf)将对逻辑回归背后的数学原理进行更加深入的讲解 From 41f429311328e339710f8e09a43b3f66338d4506 Mon Sep 17 00:00:00 2001 From: XiaojianTang <85986768+XiaojianTang@users.noreply.github.com> Date: Fri, 30 Jul 2021 11:32:43 +0800 Subject: [PATCH 05/12] optimize translation, more fluent --- .../translations/README.zh-cn.md | 64 +++++++++---------- 1 file changed, 32 insertions(+), 32 deletions(-) diff --git a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md index ac5d80ba..36273b91 100644 --- a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md +++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md @@ -82,7 +82,7 @@ 你的数据已经清洗干净并已经准备好可以进行训练了，现在需要决定你想要使用的算法来完成这项任务。 -Scikit_learn将分类任务归在了监督学习类别中，在这个类别中你将可以找到很多可以用来分类的方法。乍一看上去，有点[琳琅满目](https://scikit-learn.org/stable/supervised_learning.html)。以下这些方法都包含了分类技术： +Scikit_learn将分类任务归在了监督学习类别中，在这个类别中你可以找到很多可以用来分类的方法。乍一看上去，有点[琳琅满目](https://scikit-learn.org/stable/supervised_learning.html)。以下这些算法都可以用于分类： - 线性模型（Linear Models） - 支持向量机（Support Vector Machines） @@ -97,64 +97,64 @@ Scikit_learn将分类任务归在了监督学习类别中，在这个类别中 ### 如何选择分类器? -那么，你应该选择哪一个分类器呢？一般来说，可以多选择几个并对比他们运行后的结果。Scikit-learn提供了各种算法（包括KNeighbors、 SVC two ways、 GaussianProcessClassifier、 DecisionTreeClassifier、 RandomForestClassifier、 MLPClassifier、 AdaBoostClassifier、 GaussianNB以及QuadraticDiscrinationAnalysis）的效果[对比](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html)，并且将比较的结果进行了可视化的展示： +那么，你应该如何从中选择分类器呢？一般来说，可以选择多个分类器并对比他们的运行结果。Scikit-learn提供了各种算法（包括KNeighbors、 SVC two ways、 GaussianProcessClassifier、 DecisionTreeClassifier、 RandomForestClassifier、 MLPClassifier、 AdaBoostClassifier、 GaussianNB以及QuadraticDiscrinationAnalysis）的[对比](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html)，并且将结果进行了可视化的展示： ![各分类器比较](../images/comparison.png) > 图表来源于Scikit-learn的官方文档 -> AutoML通过在云端运行这些对比非常完美地解决的选择算法的这个问题，使得你能够根据你的数据特性选择最佳的算法。试试点击[这里](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)了解更多。 +> AutoML通过在云端运行这些算法并进行了对比，非常巧妙地解决的算法选择的问题，能帮助你根据数据集的特点来选择最佳的算法。试试点击[这里](https://docs.microsoft.com/learn/modules/automate-model-selection-with-azure-automl/?WT.mc_id=academic-15963-cxa)了解更多。 -### 一种更好的方法来选择分类器 +### 另外一种效果更佳的分类器选择方法 -不过，比起无脑地猜测，你可以下载这份[机器学习作弊表（cheatsheet）](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa)，对各算法进行对比，这是一个选择算法更有效的办法。在表中我们可以发现对于本课程中涉及的多类型的分类任务，可以有以下这些选择： +比起无脑地猜测，你可以下载这份[机器学习小抄（cheatsheet）](https://docs.microsoft.com/azure/machine-learning/algorithm-cheat-sheet?WT.mc_id=academic-15963-cxa)。这里面将各算法进行了比较，能更有效地帮助我们选择算法。根据这份小抄，我们可以找到要完成本课程中涉及的多类型的分类任务，可以有以下这些选择： ![多类型问题作弊表](../images/cheatsheet.png) -> 微软算法作弊表中关于多类型分类任务可选算法的部分 +> 微软算法小抄中部分关于多类型分类任务可选算法 -✅ 下载这份作弊表，打印出来，挂在你的墙上吧！ +✅ 下载这份小抄，并打印出来，挂在你的墙上吧！ -### 选择的过程 +### 选择的流程 -让我们看看根据我们所有的限制条件依次判断下各种方法的可行性： +让我们根据所有限制条件依次对各种算法的可行性进行判断： -- **神经网络（Neural Network）太过复杂了**。我们的数据很清晰但数据量比较小，此外我们是通过notebook在本地进行训练，神经网络对于这个任务来说过于复杂了。 -- **二分类法(two-class classifier)不可行**。我们不能使用二分类法,所以这就排除了一对多（one-vs-all）算法。 -- **决策树以及逻辑回归可行**。决策树应该是有用的，此外也可以使用逻辑回归来处理多类型数据。 -- **多类型增强决策树是用于解决其他问题的**. 多类型增强决策树最适合非参数化的任务，即任务目标是建立一个排序，这对我们当前的任务并没有作用。 +- **神经网络（Neural Network）太过复杂了**。我们的数据很清晰但数据量比较小，此外我们是通过notebook在本地进行训练的，神经网络对于这个任务来说过于复杂了。 +- **二分类法(two-class classifier)是不可行的**。我们不能使用二分类法,所以这就排除了一对多（one-vs-all）算法。 +- **可以选择决策树以及逻辑回归算法**。决策树应该是可行的，此外也可以使用逻辑回归来处理多类型数据。 +- **多类型增强决策树是用于解决其他问题的**. 多类型增强决策树最适合的是非参数化的任务，即任务目标是建立一个排序，这对我们当前的任务并没有作用。 ### 使用Scikit-learn -我们将会使用Scikit-learn来对我们的数据进行分析。然而，在Scikit-learn中使用逻辑回归也有很很多方法。可以看一看逻辑回归算法需要[传递的参数](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression)。 +我们将会使用Scikit-learn来对我们的数据进行分析。然而在Scikit-learn中使用逻辑回归也有很多方法。可以先了解一下逻辑回归算法需要[传递的参数](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression)。 -当我们需要Scikit-learn进行逻辑回归运算时，`multi_class` 以及 `solver`是最重要的两个参数，因此我们需要特别说明一下。 `multi_class` 的值是分类任务要求的某一种特定的行为。`solver`的值是我们需要使用的算法。并不是所有的solvers都可以匹配`multi_class`的值的。 +当我们需要Scikit-learn进行逻辑回归运算时，`multi_class` 以及 `solver`是最重要的两个参数，因此我们需要特别说明一下。 `multi_class` 是分类方式选择参数，而`solver`优化算法选择参数。值得注意的是，并不是所有的solvers都可以与`multi_class`参数进行匹配的。 -根据文档，在多类型问题中，训练的算法应: +根据官方文档，在多类型分类问题中: -- **使用“一对其余”(OvR)策略（scheme）**, 当`multi_class`被设置为`ovr`时 -- **使用交叉熵损失（cross entropy loss）**, 当`multi_class`被设置为`multinomial` (目前，`multinomial`只支持‘lbfgs’, ‘sag’, ‘saga’以及‘newton-cg’等 solver)时。 +- 当`multi_class`被设置为`ovr`时，将使用 **“一对其余”(OvR)策略（scheme）**。 +- 当`multi_class`被设置为`multinomial`时，则使用的是**交叉熵损失（cross entropy loss）** 作为损失函数。(注意，目前`multinomial`只支持‘lbfgs’, ‘sag’, ‘saga’以及‘newton-cg’等solver作为损失函数的优化方法) -> 🎓 其中“scheme”可以是“ovr(one-vs-rest)”也可以是“multinomial”。 因为逻辑回归本来是设计来用于进行二分类任务的，这两个scheme都可以使得逻辑回归能更好的支持多类型分类任务。[来源](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/) +> 🎓 在本课程的任务中“scheme”可以是“ovr(one-vs-rest)”也可以是“multinomial”。因为逻辑回归本来是设计来用于进行二分类任务的，这两个scheme参数的选择都可以使得逻辑回归很好的完成多类型分类任务。[来源](https://machinelearningmastery.com/one-vs-rest-and-one-vs-one-for-multi-class-classification/) > 🎓 “solver”被定义为是"用于解决优化问题的算法"。[来源](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic%20regressio#sklearn.linear_model.LogisticRegression). -Scikit-learn提供了以下这个表格来解释solver是如何应对的不同的数据结构所带来的不同的挑战的: +Scikit-learn提供了以下这个表格来解释各种solver是如何应对的不同的数据结构所带来的不同的挑战的: ![solvers](../images/solvers.png) ## 练习 - 分割数据 -你刚刚在上一节课中学习了逻辑回归，因此我们可以聚焦于此，来演练一下如何进行第一个模型的训练。首先，需要通过调用`train_test_split()`可以把你的数据分割成训练集和测试集： +因为你刚刚在上一节课中学习了逻辑回归，我们这里就通过逻辑回归算法，来演练一下如何进行你的第一个机器学习模型的训练。首先，需要通过调用`train_test_split()`方法可以把你的数据分割成训练集和测试集： ```python X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3) ``` -## 练习 - 应用逻辑回归 +## 练习 - 调用逻辑回归算法 -接着，你需要决定选用什么 _scheme_ 以及 _solver_ 来进行我们这个多类型分类的案例。这里我们使用LogisticRegression方法，并设置相应的multi_class参数，同时将solver设置为**liblinear**来进行模型训练。 +接下来，你需要决定选用什么 _scheme_ 以及 _solver_ 来进行我们这个多类型分类的案例。在这里我们使用LogisticRegression方法，并设置相应的multi_class参数，同时将solver设置为**liblinear**来进行模型训练。 -1. 创建逻辑回归，并将multi_class设置为`ovr`，同时将solver设置为 `liblinear`: +1. 创建一个逻辑回归模型，并将multi_class设置为`ovr`，同时将solver设置为 `liblinear`: ```python lr = LogisticRegression(multi_class='ovr',solver='liblinear') @@ -164,13 +164,13 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine print ("Accuracy is {}".format(accuracy)) ``` - ✅ 也可以试试其他solver比如`lbfgs`, 这通常是默认的设置 + ✅ 也可以试试其他solver比如`lbfgs`, 这也是默认参数 - > 注意, 使用Pandas的[`ravel`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ravel.html) 函数可以在需要的时候将你的数据进行降维 + > 注意, 使用Pandas的[`ravel`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.ravel.html) 方法可以在需要的时候将你的数据进行降维 - 计算结果准确率高达了**80%**! + 运算之后，可以看到准确率高达了**80%**! -1. 你也可以通过查看某一行数据（比如第50行）来观察到模型运行的情况: +1. 你也可以通过查看某一行数据（比如第50行）来观测到模型运行的情况: ```python print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}') @@ -184,9 +184,9 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine cuisine: indian ``` - ✅ 试试不同的行索引来检查一下结果吧 + ✅ 试试不同的行索引来检查一下计算的结果吧 -1. 让我们再深入研究一下，你可以检查一下本次预测的准确率: +1. 我们可以再进行一部深入的研究，检查一下本轮预测结果的准确率: ```python test= X_test.iloc[50].values.reshape(-1, 1).T @@ -210,7 +210,7 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine ✅ 你能解释下为什么模型会如此确定这是一道印度菜么？ -1. 就和你在回归的课程中所做的一样，通过输出分类的报告，我们可以得到更多的细节： +1. 和你在之前的回归的课程中所做的一样，我们也可以通过输出分类的报告得到关于模型的更多的细节： ```python y_pred = model.predict(X_test) @@ -230,7 +230,7 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine ## 挑战 -在本课程中，你使用了清洗后的数据建立了一个机器学习的模型，能够根据一系列的配料来预测菜品来自于哪个国家。请再花点时间阅读一下Scikit-learn所提供的可以用来分类数据的其他选择。同时也可以深入研究一下“solver”的概念并尝试一下理解其背后的原理。 +在本课程中，你使用了清洗后的数据建立了一个机器学习的模型，这个模型能够根据输入的一系列的配料来预测菜品来自于哪个国家。请再花点时间阅读一下Scikit-learn所提供的关于可以用来分类数据的其他方法的资料。此外，你也可以深入研究一下“solver”的概念并尝试一下理解其背后的原理。 ## [课后测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/22/) ## 回顾与自学 From 102bf8a09151923a8d38a2dc631f9958ffc5c9db Mon Sep 17 00:00:00 2001 From: XiaojianTang <85986768+XiaojianTang@users.noreply.github.com> Date: Mon, 2 Aug 2021 11:39:28 +0800 Subject: [PATCH 06/12] Update README.zh-cn.md Update Introdcution Translation Link and Tabels Format Correction --- .../translations/README.zh-cn.md | 40 +++++++++---------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md index 36273b91..83aa4fc4 100644 --- a/4-Classification/2-Classifiers-1/translations/README.zh-cn.md +++ b/4-Classification/2-Classifiers-1/translations/README.zh-cn.md @@ -2,12 +2,12 @@ 本节课程将使用你在上一个课程中所保存的全部经过均衡和清洗的菜品数据。 -你将使用这份数据集，并通过多种分类器 _在给出了各种配料后预测这是那一个国家的菜品_。在此过程中，你将学到更多能够用来调试分类任务算法的方法。 +你将使用此数据集和各种分类器，_根据一组配料预测这是哪一国家的美食_。在此过程中，你将学到更多用来权衡分类任务算法的方法 ## [课前测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/) # 准备工作 -假如你已经完成了[课程1](../1-Introduction/README.md), 确保在根目录的`/data`文件夹中有 _cleaned_cuisines.csv_ 这份文件来进行接下来的四节课程。 +假如你已经完成了[课程1](../../1-Introduction/translations/README.zh-cn.md), 确保在根目录的`/data`文件夹中有 _cleaned_cuisines.csv_ 这份文件来进行接下来的四节课程。 ## 练习 - 预测某国的菜品 @@ -68,7 +68,7 @@ 你的特征集看上去将会是这样: - | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini | | + | | almond | angelica | anise | anise_seed | apple | apple_brandy | apricot | armagnac | artemisia | artichoke | ... | whiskey | white_bread | white_wine | whole_grain_wheat_flour | wine | wood | yam | yeast | yogurt | zucchini | | -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | --- | | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | @@ -200,13 +200,13 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine 运行后的输出如下———可以发现这是一道印度菜的可能性最大，是最合理的猜测: - | | 0 | | | | | | | | | | | | | | | | | | | | | - | -------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | - | indian | 0.715851 | | | | | | | | | | | | | | | | | | | | | - | chinese | 0.229475 | | | | | | | | | | | | | | | | | | | | | - | japanese | 0.029763 | | | | | | | | | | | | | | | | | | | | | - | korean | 0.017277 | | | | | | | | | | | | | | | | | | | | | - | thai | 0.007634 | | | | | | | | | | | | | | | | | | | | | + | | 0 | + | -------: | -------: | + | indian | 0.715851 | + | chinese | 0.229475 | + | japanese | 0.029763 | + | korean | 0.017277 | + | thai | 0.007634 | ✅ 你能解释下为什么模型会如此确定这是一道印度菜么？ @@ -217,16 +217,16 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine print(classification_report(y_test,y_pred)) ``` - | precision | recall | f1-score | support | | | | | | | | | | | | | | | | | | | - | ------------ | ------ | -------- | ------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | - | chinese | 0.73 | 0.71 | 0.72 | 229 | | | | | | | | | | | | | | | | | | - | indian | 0.91 | 0.93 | 0.92 | 254 | | | | | | | | | | | | | | | | | | - | japanese | 0.70 | 0.75 | 0.72 | 220 | | | | | | | | | | | | | | | | | | - | korean | 0.86 | 0.76 | 0.81 | 242 | | | | | | | | | | | | | | | | | | - | thai | 0.79 | 0.85 | 0.82 | 254 | | | | | | | | | | | | | | | | | | - | accuracy | 0.80 | 1199 | | | | | | | | | | | | | | | | | | | | - | macro avg | 0.80 | 0.80 | 0.80 | 1199 | | | | | | | | | | | | | | | | | | - | weighted avg | 0.80 | 0.80 | 0.80 | 1199 | | | | | | | | | | | | | | | | | | + | precision | recall | f1-score | support | | + | ------------ | ------ | -------- | ------- | ---- | + | chinese | 0.73 | 0.71 | 0.72 | 229 | + | indian | 0.91 | 0.93 | 0.92 | 254 | + | japanese | 0.70 | 0.75 | 0.72 | 220 | + | korean | 0.86 | 0.76 | 0.81 | 242 | + | thai | 0.79 | 0.85 | 0.82 | 254 | + | accuracy | 0.80 | 1199 | | | + | macro avg | 0.80 | 0.80 | 0.80 | 1199 | + | weighted avg | 0.80 | 0.80 | 0.80 | 1199 | ## 挑战 From 0c4db64fc84becc69073f40c72f8065817ac788a Mon Sep 17 00:00:00 2001 From: Ravindranath Sawane <65583665+ravindranath-sawane@users.noreply.github.com> Date: Tue, 3 Aug 2021 09:32:29 +0530 Subject: [PATCH 07/12] New Scatterplot adding according to new code Changes made in the code for new Scatterplot please check --- 2-Regression/1-Tools/images/scatterplot.png | Bin 289640 -> 0 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From: Ravindranath Sawane <65583665+ravindranath-sawane@users.noreply.github.com> Date: Tue, 3 Aug 2021 09:33:15 +0530 Subject: [PATCH 09/12] Update README.md --- 2-Regression/1-Tools/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/2-Regression/1-Tools/README.md b/2-Regression/1-Tools/README.md index 2bd3f7f2..69986fba 100644 --- a/2-Regression/1-Tools/README.md +++ b/2-Regression/1-Tools/README.md @@ -183,6 +183,9 @@ In a new code cell, load the diabetes dataset by calling `load_diabetes()`. The ```python plt.scatter(X_test, y_test, color='black') plt.plot(X_test, y_pred, color='blue', linewidth=3) + plt.xlabel('Scaled BMIs') + plt.ylabel('Disease Progression') + plt.title('A Graph Plot Showing Diabetes Progression Against BMI') plt.show() ``` From d5a71438efdb1895bc13c15ae5267b7b5e12ab3e Mon Sep 17 00:00:00 2001 From: RyanXin Date: Tue, 3 Aug 2021 16:02:52 +0800 Subject: [PATCH 10/12] Fix minor typos. --- 2-Regression/4-Logistic/README.md | 2 +- 3-Web-App/1-Web-App/README.md | 2 +- 4-Classification/README.md | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md index 62697103..a2a938a7 100644 --- a/2-Regression/4-Logistic/README.md +++ b/2-Regression/4-Logistic/README.md @@ -140,7 +140,7 @@ Now that we have an idea of the relationship between the binary categories of co > **🧮 Show Me The Math** > -> Remember how linear regression often used ordinary least squares to arrive at a value? Logistic regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like thus: +> Remember how linear regression often used ordinary least squares to arrive at a value? Logistic regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like this: > > ![logistic function](images/sigmoid.png) > diff --git a/3-Web-App/1-Web-App/README.md b/3-Web-App/1-Web-App/README.md index c4b406fa..2e409e78 100644 --- a/3-Web-App/1-Web-App/README.md +++ b/3-Web-App/1-Web-App/README.md @@ -199,7 +199,7 @@ Now you can build a Flask app to call your model and return similar results, but 2. Create **index.html** in _templates_ directory. 3. Create **styles.css** in _static/css_ directory. -1. Build out the _styles.css__ file with a few styles: +1. Build out the _styles.css_ file with a few styles: ```css body { diff --git a/4-Classification/README.md b/4-Classification/README.md index f6133aa1..73d83beb 100644 --- a/4-Classification/README.md +++ b/4-Classification/README.md @@ -8,7 +8,7 @@ In Asia and India, food traditions are extremely diverse, and very delicious! Le ## What you will learn -In this section, you will build on the skills you learned in the first part of this curriculum all about regressionn to learn about other classifiers you can use that will help you learn about your data. +In this section, you will build on the skills you learned in the first part of this curriculum all about regression to learn about other classifiers you can use that will help you learn about your data. > There are useful low-code tools that can help you learn about working with classification models. Try [Azure ML for this task](https://docs.microsoft.com/learn/modules/create-classification-model-azure-machine-learning-designer/?WT.mc_id=academic-15963-cxa) From 0ca90cc26988282b72c60043d9aacab2ec53393c Mon Sep 17 00:00:00 2001 From: Jay Patel <59785863+jaypatel31@users.noreply.github.com> Date: Tue, 3 Aug 2021 19:10:11 +0530 Subject: [PATCH 11/12] Feature Explanation Rectified --- 1-Introduction/4-techniques-of-ML/README.md | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/1-Introduction/4-techniques-of-ML/README.md b/1-Introduction/4-techniques-of-ML/README.md index b29c7b18..70b96000 100644 --- a/1-Introduction/4-techniques-of-ML/README.md +++ b/1-Introduction/4-techniques-of-ML/README.md @@ -40,9 +40,13 @@ To be able to answer your question with any kind of certainty, you need a good a ✅ After collecting and processing your data, take a moment to see if its shape will allow you to address your intended question. It may be that the data will not perform well in your given task, as we discover in our [Clustering](../../5-Clustering/1-Visualize/README.md) lessons! -### Selecting your feature variable +### Features and Target + +A [feature](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) is a measurable property of your data. In many datasets it is expressed as a column heading like 'date' 'size' or 'color'. Your feature variable, usually represented as `X` in code, represent the input variable which will be used to train model. -A [feature](https://www.datasciencecentral.com/profiles/blogs/an-introduction-to-variable-and-feature-selection) is a measurable property of your data. In many datasets it is expressed as a column heading like 'date' 'size' or 'color'. Your feature variable, usually represented as `y` in code, represents the answer to the question you are trying to ask of your data: in December, what **color** pumpkins will be cheapest? in San Francisco, what neighborhoods will have the best real estate **price**? +A target is a thing you are trying to predict. Target usually represented as `y` in code, represents the answer to the question you are trying to ask of your data: in December, what **color** pumpkins will be cheapest? in San Francisco, what neighborhoods will have the best real estate **price**? Sometimes target is also referred as label attribute. + +### Selecting your feature variable 🎓 **Feature Selection and Feature Extraction** How do you know which variable to choose when building a model? You'll probably go through a process of feature selection or feature extraction to choose the right variables for the most performant model. They're not the same thing, however: "Feature extraction creates new features from functions of the original features, whereas feature selection returns a subset of the features." ([source](https://wikipedia.org/wiki/Feature_selection)) @@ -68,7 +72,7 @@ Depending on your question and the nature of your data, you will choose a method ### Train a model -Armed with your training data, you are ready to 'fit' it to create a model. You will notice that in many ML libraries you will find the code 'model.fit' - it is at this time that you send in your data as an array of values (usually 'X') and a feature variable (usually 'y'). +Armed with your training data, you are ready to 'fit' it to create a model. You will notice that in many ML libraries you will find the code 'model.fit' - it is at this time that you send in your feature variable as an array of values (usually 'X') and a target variable (usually 'y'). ### Evaluate the model From 2cb01b28428f4bf1c1e76ce18323662dbb1f47a7 Mon Sep 17 00:00:00 2001 From: LAKSHAY AGGARWAL <48948478+LakshayAggarwal25@users.noreply.github.com> Date: Tue, 3 Aug 2021 19:36:40 +0530 Subject: [PATCH 12/12] Updated table visualization Updated table visualization for output table on line number 298 --- 7-TimeSeries/2-ARIMA/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/7-TimeSeries/2-ARIMA/README.md b/7-TimeSeries/2-ARIMA/README.md index d54a781b..97d0c49a 100644 --- a/7-TimeSeries/2-ARIMA/README.md +++ b/7-TimeSeries/2-ARIMA/README.md @@ -295,7 +295,7 @@ Walk-forward validation is the gold standard of time series model evaluation and eval_df.head() ``` - ```output + Output | | | timestamp | h | prediction | actual | | --- | ---------- | --------- | --- | ---------- | -------- | | 0 | 2014-12-30 | 00:00:00 | t+1 | 3,008.74 | 3,023.00 | @@ -303,7 +303,7 @@ Walk-forward validation is the gold standard of time series model evaluation and | 2 | 2014-12-30 | 02:00:00 | t+1 | 2,900.17 | 2,899.00 | | 3 | 2014-12-30 | 03:00:00 | t+1 | 2,917.69 | 2,886.00 | | 4 | 2014-12-30 | 04:00:00 | t+1 | 2,946.99 | 2,963.00 | - ``` + Observe the hourly data's prediction, compared to the actual load. How accurate is this?