Mushroom Classification

### Context

Although this dataset was originally contributed to the UCI Machine Learning repository nearly 30 years ago, mushroom hunting (otherwise known as "shrooming") is enjoying new peaks in popularity. Learn which features spell certain death and which are most palatable in this dataset of mushroom characteristics. And how certain can your model be?

### Content

This dataset includes descriptions of hypothetical samples corresponding to 23 species of gilled mushrooms in the Agaricus and Lepiota Family Mushroom drawn from The Audubon Society Field Guide to North American Mushrooms (1981). Each species is identified as definitely edible, definitely poisonous, or of unknown edibility and not recommended. This latter class was combined with the poisonous one. The Guide clearly states that there is no simple rule for determining the edibility of a mushroom; no rule like "leaflets three, let it be'' for Poisonous Oak and Ivy.

- **Time period**: Donated to UCI ML 27 April 1987

### Inspiration

- What types of machine learning models perform best on this dataset?

- Which features are most indicative of a poisonous mushroom?

### Acknowledgements

This dataset was originally donated to the UCI Machine Learning repository. You can learn more about past research using the data [here][1].

#[Start a new kernel][2]

[1]: https://archive.ics.uci.edu/ml/datasets/Mushroom
[2]: https://www.kaggle.com/uciml/mushroom-classification/kernels?modal=true

尽管此数据集最初于30年前贡献给了UCI机器学习存储库，但蘑菇采集（亦称“寻菇”）正迎来新的流行高潮。本数据集揭示了哪些特征预示着致命的后果，哪些特征最为可口。您的模型能有多大的确定性？

### 数据内容

本数据集包含了对虚构样本的描述，这些样本对应于来自《奥杜邦北美蘑菇野外指南》（1981年）中的23种伞菌科蘑菇，包括Agaricus和Lepiota属。每种蘑菇均被明确标识为可食用、有毒或食用性未知且不建议食用，后两类被合并。指南明确指出，没有简单的规则可以用来判断蘑菇的可食用性；没有类似于“叶子三片，任其生长”这样的规则，适用于有毒的橡树和常春藤。

- **时间线**：于1987年4月27日捐赠至UCI ML

### 灵感

- 哪种机器学习模型在此数据集上表现最佳？
- 哪些特征最能指示有毒蘑菇？

### 致谢

本数据集最初捐赠给了UCI机器学习存储库。您可以通过[此处](https://archive.ics.uci.edu/ml/datasets/Mushroom)了解更多关于过去对该数据集的研究。

[启动新内核](https://www.kaggle.com/uciml/mushroom-classification/kernels?modal=true)