根据这里的说法，sklearn不能处理分类变量。建议使用一种热编码来处理这些特性。然而，我不明白一个热编码怎么能有帮助？例如，country=USA或China或England被转换为country=USA是真是假，新功能“country==USA”仍然是分类的（只能取0或1）。这不会改变任何事情。Sklearn仍然将0或1视为数值。

对于这里的一个真实示例，我转换了数据：

human,warm-blooded,hair,yes,no,no,yes,no,mammal
python,cold-blooded,scales,no,no,no,no,yes,reptile
salmon,cold-blooded,scales,no,yes,no,no,no,fish
whale,warm-blooded,hair,yes,yes,no,no,no,mammal
frog,cold-blooded,none,no,semi,no,yes,yes,amphibian
komodo dragon,cold-blooded,scales,no,no,no,yes,no,reptile
bat,warm-blooded,hair,yes,no,yes,yes,yes,mammal
pigeon,warm-blooded,feathers,no,no,yes,yes,no,bird
cat,warm-blooded,fur,yes,no,no,yes,no,mammal
leopard shark,cold-blooded,scales,yes,yes,no,no,no,fish
turtle,cold-blooded,scales,no,semi,no,yes,no,reptile
penguin,warm-blooded,feathers,no,semi,no,yes,no,bird
porcupine,warm-blooded,quills,yes,no,no,yes,yes,mammal
eel,cold-blooded,scales,no,yes,no,no,no,fish
salamander,cold-blooded,none,no,semi,no,yes,yes,amphibian
gila monster,cold-blooded,scales,no,no,no,yes,yes,

进入

[[1. 0. 1. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 0. 1. 0. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 1. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 0. 0. 1. 0. 0. 0.]
 [1. 0. 0. 1. 0. 1. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [0. 1. 1. 0. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 0. 1. 0. 0. 0.]
 [0. 1. 1. 0. 0. 0. 1. 1. 0. 0. 1. 1. 0. 1. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 1. 0. 0. 0. 0.]
 [1. 0. 0. 0. 1. 1. 0. 0. 1. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 1. 0. 1. 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 1. 0. 0. 1. 1. 0. 0. 1. 1. 0. 1. 0. 0. 0. 0. 0.]
 [1. 0. 1. 0. 0. 0. 1. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 1. 0.]
 [1. 0. 0. 0. 1. 1. 0. 1. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1.]
 [1. 0. 0. 1. 0. 1. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 1. 0. 0.]
 [1. 0. 1. 0. 0. 1. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 1.]]

然后建立一个决策树，就像

决策树（点击打开）分割点仍然很可笑（比如分娩=否）