Iris Dataset

### Context

Based on Fisher's linear discriminant model, this data set became a typical test case for many statistical classification techniques in machine learning such as support vector machines.

### Content

The Iris flower data set or Fisher's Iris data set is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper The use of multiple measurements in taxonomic problems as an example of linear discriminant analysis.[1] It is sometimes called Anderson's Iris data set because Edgar Anderson collected the data to quantify the morphologic variation of Iris flowers of three related species.[2] Two of the three species were collected in the Gaspé Peninsula "all from the same pasture, and picked on the same day and measured at the same time by the same person with the same apparatus".[3]

The data set consists of 50 samples from each of three species of Iris (Iris setosa, Iris virginica and Iris versicolor). Four features were measured from each sample: the length and the width of the sepals and petals, in centimetres. Based on the combination of these four features, Fisher developed a linear discriminant model to distinguish the species from each other.

### Acknowledgements

description taken from Wiki
Would like to thank Dr. Jason Brownlee who has explained all the examples very nicely and clearly!

基于费舍尔线性判别模型，本数据集成为机器学习中众多统计分类技术的典型测试案例，如支持向量机等。

内容

鸢尾花数据集，或称费舍尔鸢尾花数据集，是由英国统计学家兼生物学家罗纳德·费舍尔在其1936年发表的论文《在分类问题中使用多种测量方法》（The use of multiple measurements in taxonomic problems）中提出的多元数据集。有时亦称为安德森鸢尾花数据集，因为爱德华·安德森收集了这些数据以量化三种相关物种的鸢尾花形态学变异。[1]其中两种物种采集于加斯佩半岛，'均来自同一牧场，在同一天由同一人使用同一设备采摘并测量'。[3]

该数据集包含三个物种（鸢尾花赛塔、鸢尾花弗吉尼亚和鸢尾花杂色）各50个样本。每个样本测量了四个特征：花萼和花瓣的长度及宽度，单位为厘米。基于这四个特征的组合，费舍尔开发了一个线性判别模型，用以区分不同物种。

致谢

描述取自维基百科
感谢贾森·布朗利博士，他清晰、详尽地解释了所有示例！