인공지능/머신러닝
[ML Algorithm] label encoding 과 one hot encoding
RosyPark
2019. 10. 6. 20:46
1. label encoding
- 단순하게 레이블링 해주는 것
- 하지만 머신러닝시 가중치로 되어 데이터 정확도에 혼선을 줄 수 있음
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from sklearn.preprocessing import LabelEncoder
import pandas as pd
import numpy as np
array = np.array([["paris","paris","seoul"],["newyork","paris","seoul"],["seoul","paris","paris"],["seoul","paris","paris"]])
df = pd.DataFrame(array, columns = ['A','B','C'])
print(df)
"""
A B C
0 paris paris seoul
1 newyork paris seoul
2 seoul paris paris
3 seoul paris paris
"""
le = LabelEncoder().fit_transform(df['A'])
print(le)
"""
[1 0 2 2]
"""
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cs |
2. one-hot encoding
- 1과 0으로 나타내는 방법
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from sklearn.preprocessing import OneHotEncoder
import pandas as pd
import numpy as np
array = np.array([["paris","paris","seoul"],["newyork","paris","seoul"],["seoul","paris","paris"],["seoul","paris","paris"]])
df = pd.DataFrame(array, columns = ['A','B','C'])
print(df)
"""
A B C
0 paris paris seoul
1 newyork paris seoul
2 seoul paris paris
3 seoul paris paris
"""
data = np.array(df['A'])
data = data.reshape(-1,1)
le = OneHotEncoder().fit_transform(data)
print(le)
"""
(0, 1) 1.0
(1, 0) 1.0
(2, 2) 1.0
(3, 2) 1.0
"""
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cs |