PROSAGA码农传奇-数据隔离-隔离林：分类数据

隔离林：分类数据

作者: 只怕再见是故人
发布时间: 2024-04-16 07:18:57 (7天前)

您应该将分类数据编码为数字表示形式。

有许多方法可以编码分类数据，但是我建议您从

sklearn.preprocessing.LabelEncoder如果基数高而sklearn.preprocessing.OneHotEncoder基数低。

这里是一个用法示例：

```
from numpy import argmax
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
# define example
data = ['cold', 'cold', 'warm', 'cold', 'hot', 'hot', 'warm', 'cold', 'warm', 'hot']
values = array(data)
print(values)
# integer encode
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(values)
print(integer_encoded)
# binary encode
onehot_encoder = OneHotEncoder(sparse=False)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
print(onehot_encoded)
# invert first example
inverted = label_encoder.inverse_transform([argmax(onehot_encoded[0, :])])
print(inverted)
```

输出：

```
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
['cold' 'cold' 'warm' 'cold' 'hot' 'hot' 'warm' 'cold' 'warm' 'hot']

[0 0 2 0 1 1 2 0 2 1]

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

['cold']
```