python中如何实现将数据分成训练集与测试集的方法

#导入相应的库（对数据库进行切分需要用到的库是sklearn.model_selection 中的 train_test_split）import numpy as npfrom sklearn.model_selection import train_test_split #首先，读取.CSV文件成矩阵的形式。my_matrix = np.loadtxt(open("xxxxxx.csv"),delimiter=",",skiprows=0) #对于矩阵而言，将矩阵倒数第一列之前的数值给了X（输入数据），将矩阵大最后一列的数值给了y（标签）X, y = my_matrix[:,:-1],my_matrix[:,-1] #利用train_test_split方法，将X,y随机划分问，训练集（X_train），训练集标签（X_test），测试卷（y_train）， 测试集标签（y_test），安训练集：测试集=7:3的 概率划分，到此步骤，可以直接对数据进行处理X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) #此步骤，是为了将训练集与数据集的数据分别保存为CSV文件 #np.column_stack将两个矩阵进行组合连接train= np.column_stack((X_train,y_train)) #numpy.savetxt 将txt文件保存为。csv结尾的文件numpy.savetxt('train_usual.csv',train, delimiter = ',')test = np.column_stack((X_test, y_test))numpy.savetxt('test_usual.csv', test, delimiter = ',')

import numpy as npfrom sklearn.model_selection import train_test_splitmy_matrix = np.loadtxt(open("xxxxx.csv"),delimiter=",",skiprows=0)X, y = my_matrix[:,:-1],my_matrix[:,-1]X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)train= np.column_stack((X_train,y_train))numpy.savetxt('train_usual.csv',train, delimiter = ',')test = np.column_stack((X_test, y_test))numpy.savetxt('test_usual.csv', test, delimiter = ',')