在机器学习中使用三种不同的标签

Question

我真的是机器学习的新手。我正在检查在电子邮件中分离垃圾邮件或火腿值的代码。我为另一个数据集设置代码时遇到问题。所以，我的数据集不仅仅有ham或spam值。我有两个不同的分类值（年龄和性别）。当我尝试在下面的代码块中使用2个分类值时，我得到了一个错误，解包的值太多。我怎么才能把我的全部价值观

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test  = train_test_split(messages_bow, import_data['age'], import_data['gender'], test_size = 0.20, random_state = 0)

整体代码：

import numpy as np
import pandas
import nltk
from nltk.corpus import stopwords
import string

# Import Data.
import_data = pandas.read_csv('/root/Desktop/%20/%100.csv' , encoding='cp1252') 

# To See Columns Headers.
print(import_data.columns) 

# To Remove Duplications.
import_data.drop_duplicates(inplace = True) 

# To Find Data Size.
print(import_data.shape) 


#Tokenization (a list of tokens), will be used as the analyzer
#1.Punctuations are [!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~]
#2.Stop words in natural language processing, are useless words (data).
def process_text(text):
    '''
    What will be covered:
    1. Remove punctuation
    2. Remove stopwords
    3. Return list of clean text words
    '''

    #1
    nopunc = [char for char in text if char not in string.punctuation]
    nopunc = ''.join(nopunc)

    #2
    clean_words = [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]

    #3
    return clean_words

#Show the Tokenization (a list of tokens )
print(import_data['text'].head().apply(process_text)) 

# Convert the text into a matrix of token counts.
from sklearn.feature_extraction.text import CountVectorizer 
messages_bow = CountVectorizer(analyzer=process_text).fit_transform(import_data['text']) 

#Split data into 80% training & 20% testing data sets

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test  = train_test_split(messages_bow, import_data['gender'], import_data['frequency'], test_size = 0.20, random_state = 0)

#Get the shape of messages_bow
print(messages_bow.shape)

Answer 1

train_test_split将传递给它的每个参数拆分为训练集和测试集。由于要拆分三种不同类型的数据，因此需要6个变量：

X_train, X_test, age_train, age_test, gender_train, gender_test = train_test_split(messages_bow, import_data['age'], import_data['gender'], test_size=0.20, random_state=0)