您应该将分类数据编码为数字表示。
有很多方法可以对分类数据进行编码,但我建议您先开始使用
sklearn.preprocessing.LabelEncoder 如果基数很高而且 sklearn.preprocessing.OneHotEncoder 如果基数低。
sklearn.preprocessing.LabelEncoder
sklearn.preprocessing.OneHotEncoder
这是一个用法示例:
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']