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@ -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。 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/) ## [课前测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)
# 准备工作 # 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 ## 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: 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 | | 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 | | 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: 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 | | | | | | | | | | | | | | | | | | | | | | | 0 |
| -------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | -------: | -------: |
| indian | 0.715851 | | | | | | | | | | | | | | | | | | | | | | indian | 0.715851 |
| chinese | 0.229475 | | | | | | | | | | | | | | | | | | | | | | chinese | 0.229475 |
| japanese | 0.029763 | | | | | | | | | | | | | | | | | | | | | | japanese | 0.029763 |
| korean | 0.017277 | | | | | | | | | | | | | | | | | | | | | | korean | 0.017277 |
| thai | 0.007634 | | | | | | | | | | | | | | | | | | | | | | 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？ ✅ 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)) print(classification_report(y_test,y_pred))
``` ```
| precision | recall | f1-score | support | | | | | | | | | | | | | | | | | | | | precision | recall | f1-score | support | |
| ------------ | ------ | -------- | ------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | ------------ | ------ | -------- | ------- | ---- |
| chinese | 0.73 | 0.71 | 0.72 | 229 | | | | | | | | | | | | | | | | | | | chinese | 0.73 | 0.71 | 0.72 | 229 |
| indian | 0.91 | 0.93 | 0.92 | 254 | | | | | | | | | | | | | | | | | | | indian | 0.91 | 0.93 | 0.92 | 254 |
| japanese | 0.70 | 0.75 | 0.72 | 220 | | | | | | | | | | | | | | | | | | | japanese | 0.70 | 0.75 | 0.72 | 220 |
| korean | 0.86 | 0.76 | 0.81 | 242 | | | | | | | | | | | | | | | | | | | korean | 0.86 | 0.76 | 0.81 | 242 |
| thai | 0.79 | 0.85 | 0.82 | 254 | | | | | | | | | | | | | | | | | | | thai | 0.79 | 0.85 | 0.82 | 254 |
| accuracy | 0.80 | 1199 | | | | | | | | | | | | | | | | | | | | | accuracy | 0.80 | 1199 | | |
| macro avg | 0.80 | 0.80 | 0.80 | 1199 | | | | | | | | | | | | | | | | | | | macro avg | 0.80 | 0.80 | 0.80 | 1199 |
| weighted avg | 0.80 | 0.80 | 0.80 | 1199 | | | | | | | | | | | | | | | | | | | weighted avg | 0.80 | 0.80 | 0.80 | 1199 |
## 挑战 ## 挑战

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