數組在numpy/sklearn數據集中的含義是什麼？ python

Question

從sklearn中的樸素貝葉斯教程有iris數據集的例子，但它看起來太神祕了，有人可以幫助啓發我嗎？

iris.data是什麼意思？爲什麼有4列？

iris.target是什麼意思？爲什麼他們是0，1和2的平面陣列？

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]] 

從iris.target，它返回的0，1和2秒另一個陣列。 這是什麼意思？ 從sklearn進口集 虹膜= datasets.load_iris（） 打印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] 

Answer 1

虹膜是公知的Fisher's Iris數據集。他測量了三種鳶尾花的萼片和花瓣（兩朵花）的長度和寬度。每行包含一朵花的測量結果，並且每種類型有50朵花的測量結果，因此虹膜數據的尺寸。在iris.target中花的實際類型被編碼爲0,1或2;您可以從iris.target_name中恢復實際的物種名稱（作爲字符串）。費希爾表明他的新判別方法可以根據萼片和花瓣的測量結果分離出三種物種，並且從那時起它就成爲一種標準的分類數據組。

Td; dr：樣本數據。每行有四個屬性的示例;共150個例子。類別標籤單獨存儲並編碼爲整數。

Docs here：http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_iris.html#sklearn.datasets.load_iris