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@ -2,12 +2,12 @@
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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。
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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。
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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
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## [课前测验](https://jolly-sea-0a877260f.azurestaticapps.net/quiz/21/)
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# 准备工作
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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。
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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。
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## 练习 - 预测某国的菜品
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@ -68,7 +68,7 @@
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你的特征集看上去将会是这样:
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| 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 | |
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| | 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 |
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| -----: | -------: | ----: | ---------: | ----: | -----------: | ------: | -------: | --------: | --------: | ---: | ------: | ----------: | ---------: | ----------------------: | ---: | ---: | ---: | ----: | -----: | -------: | --- |
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| 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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| 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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@ -200,13 +200,13 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
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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:
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| | 0 | | | | | | | | | | | | | | | | | | | | |
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| -------: | -------: | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| indian | 0.715851 | | | | | | | | | | | | | | | | | | | | |
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| chinese | 0.229475 | | | | | | | | | | | | | | | | | | | | |
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| japanese | 0.029763 | | | | | | | | | | | | | | | | | | | | |
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| korean | 0.017277 | | | | | | | | | | | | | | | | | | | | |
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| thai | 0.007634 | | | | | | | | | | | | | | | | | | | | |
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| | 0 |
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| -------: | -------: |
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| indian | 0.715851 |
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| chinese | 0.229475 |
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| japanese | 0.029763 |
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| korean | 0.017277 |
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| thai | 0.007634 |
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✅ 你能解释下为什么模型会如此确定这是一道印度菜么？
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@ -217,16 +217,16 @@ X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisine
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print(classification_report(y_test,y_pred))
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```
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| precision | recall | f1-score | support | | | | | | | | | | | | | | | | | | |
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| ------------ | ------ | -------- | ------- | ---- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
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| chinese | 0.73 | 0.71 | 0.72 | 229 | | | | | | | | | | | | | | | | | |
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| indian | 0.91 | 0.93 | 0.92 | 254 | | | | | | | | | | | | | | | | | |
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| japanese | 0.70 | 0.75 | 0.72 | 220 | | | | | | | | | | | | | | | | | |
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| korean | 0.86 | 0.76 | 0.81 | 242 | | | | | | | | | | | | | | | | | |
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| thai | 0.79 | 0.85 | 0.82 | 254 | | | | | | | | | | | | | | | | | |
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| accuracy | 0.80 | 1199 | | | | | | | | | | | | | | | | | | | |
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| macro avg | 0.80 | 0.80 | 0.80 | 1199 | | | | | | | | | | | | | | | | | |
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| weighted avg | 0.80 | 0.80 | 0.80 | 1199 | | | | | | | | | | | | | | | | | |
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| precision | recall | f1-score | support | |
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| ------------ | ------ | -------- | ------- | ---- |
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| chinese | 0.73 | 0.71 | 0.72 | 229 |
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| indian | 0.91 | 0.93 | 0.92 | 254 |
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| japanese | 0.70 | 0.75 | 0.72 | 220 |
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| korean | 0.86 | 0.76 | 0.81 | 242 |
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| thai | 0.79 | 0.85 | 0.82 | 254 |
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| accuracy | 0.80 | 1199 | | |
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| macro avg | 0.80 | 0.80 | 0.80 | 1199 |
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| weighted avg | 0.80 | 0.80 | 0.80 | 1199 |
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## 挑战
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XiaojianTang
3 years ago
committed by
GitHub