来自sklearn的Naive Bayes教程中有iris数据集上的示例，但它看起来太神秘了，有人可以帮助启发我吗？
iris.data是什么意思？为什么有4列？
iris.target是什么意思？为什么它们是0s，1s和2s的平面阵列？
from sklearn import datasets
iris = datasets.load_iris()
print iris.data
[OUT]：
[[ 5.1 3.5 1.4 0.2]
[ 4.9 3. 1.4 0.2]
[ 4.7 3.2 1.3 0.2]
[ 4.6 3.1 1.5 0.2]
[ 5. 3.6 1.4 0.2]
[ 5.4 3.9 1.7 0.4]
[ 4.6 3.4 1.4 0.3]
[ 5. 3.4 1.5 0.2]
[ 4.4 2.9 1.4 0.2]
[ 4.9 3.1 1.5 0.1]
[ 5.4 3.7 1.5 0.2]
[ 4.8 3.4 1.6 0.2]
[ 4.8 3. 1.4 0.1]
[ 4.3 3. 1.1 0.1]
[ 5.8 4. 1.2 0.2]
[ 5.7 4.4 1.5 0.4]
[ 5.4 3.9 1.3 0.4]
[ 5.1 3.5 1.4 0.3]
[ 5.7 3.8 1.7 0.3]
[ 5.1 3.8 1.5 0.3]
[ 5.4 3.4 1.7 0.2]
[ 5.1 3.7 1.5 0.4]
[ 4.6 3.6 1. 0.2]
[ 5.1 3.3 1.7 0.5]
[ 4.8 3.4 1.9 0.2]
[ 5. 3. 1.6 0.2]
[ 5. 3.4 1.6 0.4]
[ 5.2 3.5 1.5 0.2]
[ 5.2 3.4 1.4 0.2]
[ 4.7 3.2 1.6 0.2]
[ 4.8 3.1 1.6 0.2]
[ 5.4 3.4 1.5 0.4]
[ 5.2 4.1 1.5 0.1]
[ 5.5 4.2 1.4 0.2]
[ 4.9 3.1 1.5 0.1]
[ 5. 3.2 1.2 0.2]
[ 5.5 3.5 1.3 0.2]
[ 4.9 3.1 1.5 0.1]
[ 4.4 3. 1.3 0.2]
[ 5.1 3.4 1.5 0.2]
[ 5. 3.5 1.3 0.3]
[ 4.5 2.3 1.3 0.3]
[ 4.4 3.2 1.3 0.2]
[ 5. 3.5 1.6 0.6]
[ 5.1 3.8 1.9 0.4]
[ 4.8 3. 1.4 0.3]
[ 5.1 3.8 1.6 0.2]
[ 4.6 3.2 1.4 0.2]
[ 5.3 3.7 1.5 0.2]
[ 5. 3.3 1.4 0.2]
[ 7. 3.2 4.7 1.4]
[ 6.4 3.2 4.5 1.5]
[ 6.9 3.1 4.9 1.5]
[ 5.5 2.3 4. 1.3]
[ 6.5 2.8 4.6 1.5]
[ 5.7 2.8 4.5 1.3]
[ 6.3 3.3 4.7 1.6]
[ 4.9 2.4 3.3 1. ]
[ 6.6 2.9 4.6 1.3]
[ 5.2 2.7 3.9 1.4]
[ 5. 2. 3.5 1. ]
[ 5.9 3. 4.2 1.5]
[ 6. 2.2 4. 1. ]
[ 6.1 2.9 4.7 1.4]
[ 5.6 2.9 3.6 1.3]
[ 6.7 3.1 4.4 1.4]
[ 5.6 3. 4.5 1.5]
[ 5.8 2.7 4.1 1. ]
[ 6.2 2.2 4.5 1.5]
[ 5.6 2.5 3.9 1.1]
[ 5.9 3.2 4.8 1.8]
[ 6.1 2.8 4. 1.3]
[ 6.3 2.5 4.9 1.5]
[ 6.1 2.8 4.7 1.2]
[ 6.4 2.9 4.3 1.3]
[ 6.6 3. 4.4 1.4]
[ 6.8 2.8 4.8 1.4]
[ 6.7 3. 5. 1.7]
[ 6. 2.9 4.5 1.5]
[ 5.7 2.6 3.5 1. ]
[ 5.5 2.4 3.8 1.1]
[ 5.5 2.4 3.7 1. ]
[ 5.8 2.7 3.9 1.2]
[ 6. 2.7 5.1 1.6]
[ 5.4 3. 4.5 1.5]
[ 6. 3.4 4.5 1.6]
[ 6.7 3.1 4.7 1.5]
[ 6.3 2.3 4.4 1.3]
[ 5.6 3. 4.1 1.3]
[ 5.5 2.5 4. 1.3]
[ 5.5 2.6 4.4 1.2]
[ 6.1 3. 4.6 1.4]
[ 5.8 2.6 4. 1.2]
[ 5. 2.3 3.3 1. ]
[ 5.6 2.7 4.2 1.3]
[ 5.7 3. 4.2 1.2]
[ 5.7 2.9 4.2 1.3]
[ 6.2 2.9 4.3 1.3]
[ 5.1 2.5 3. 1.1]
[ 5.7 2.8 4.1 1.3]
[ 6.3 3.3 6. 2.5]
[ 5.8 2.7 5.1 1.9]
[ 7.1 3. 5.9 2.1]
[ 6.3 2.9 5.6 1.8]
[ 6.5 3. 5.8 2.2]
[ 7.6 3. 6.6 2.1]
[ 4.9 2.5 4.5 1.7]
[ 7.3 2.9 6.3 1.8]
[ 6.7 2.5 5.8 1.8]
[ 7.2 3.6 6.1 2.5]
[ 6.5 3.2 5.1 2. ]
[ 6.4 2.7 5.3 1.9]
[ 6.8 3. 5.5 2.1]
[ 5.7 2.5 5. 2. ]
[ 5.8 2.8 5.1 2.4]
[ 6.4 3.2 5.3 2.3]
[ 6.5 3. 5.5 1.8]
[ 7.7 3.8 6.7 2.2]
[ 7.7 2.6 6.9 2.3]
[ 6. 2.2 5. 1.5]
[ 6.9 3.2 5.7 2.3]
[ 5.6 2.8 4.9 2. ]
[ 7.7 2.8 6.7 2. ]
[ 6.3 2.7 4.9 1.8]
[ 6.7 3.3 5.7 2.1]
[ 7.2 3.2 6. 1.8]
[ 6.2 2.8 4.8 1.8]
[ 6.1 3. 4.9 1.8]
[ 6.4 2.8 5.6 2.1]
[ 7.2 3. 5.8 1.6]
[ 7.4 2.8 6.1 1.9]
[ 7.9 3.8 6.4 2. ]
[ 6.4 2.8 5.6 2.2]
[ 6.3 2.8 5.1 1.5]
[ 6.1 2.6 5.6 1.4]
[ 7.7 3. 6.1 2.3]
[ 6.3 3.4 5.6 2.4]
[ 6.4 3.1 5.5 1.8]
[ 6. 3. 4.8 1.8]
[ 6.9 3.1 5.4 2.1]
[ 6.7 3.1 5.6 2.4]
[ 6.9 3.1 5.1 2.3]
[ 5.8 2.7 5.1 1.9]
[ 6.8 3.2 5.9 2.3]
[ 6.7 3.3 5.7 2.5]
[ 6.7 3. 5.2 2.3]
[ 6.3 2.5 5. 1.9]
[ 6.5 3. 5.2 2. ]
[ 6.2 3.4 5.4 2.3]
[ 5.9 3. 5.1 1.8]]
从iris.target开始，它返回0s，1s和2s的另一个数组。 他们是什么意思？
来自sklearn导入数据集
iris = datasets.load_iris（）
print iris.target
[OUT]：
[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 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2]
答案 0 :(得分：4)
Iris是众所周知的Fisher's Iris数据集。他测量了三种鸢尾的萼片和花瓣（花的两部分）的长度和宽度。每行包含来自一朵花的测量结果，并且每种类型的50朵花的测量值，因此是iris.data的尺寸。花的实际类型在iris.target中编码为0,1或2;你可以从iris.target_name恢复实际的物种名称（作为字符串）。
费希尔表示，他当时的新判别方法可以根据它们的萼片和花瓣测量值将这三种物种分开，从那时起它就成了标准的分类数据集。Td; dr：样本数据。每行一个例子，有四个属性;总共150个例子。类标签分开存储，并编码为整数。