sklearn-train_test_split随机划分训练集和

sklearn.model_selection.train_test_split随机划分训练集和测试集

官网文档：

http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html#sklearn.model_selection.train_test_split

导入：

from sklearn.model_selection import train_test_split

一般形式：

train_test_split是交叉验证中常用的函数，功能是从样本中随机的按比例选取train data和testdata，形式为：

X_train,X_test, y_train, y_test =
cross_validation.train_test_split(train_data,train_target,test_size=0.4, random_state=0)

参数解释：

train_data：所要划分的样本特征集
train_target：所要划分的样本结果
test_size：样本占比，如果是整数的话就是样本的数量
random_state：是随机数的种子。
随机数种子：其实就是该组随机数的编号，在需要重复试验的时候，保证得到一组一样的随机数。比如你每次都填1，其他参数一样的情况下你得到的随机数组是一样的。但填0或不填，每次都会不一样。
随机数的产生取决于种子，随机数和种子之间的关系遵从以下两个规则：
种子不同，产生不同的随机数；种子相同，即使实例不同也产生相同的随机数。

示例

import pandas as pd
from sklearn.model_selection import train_test_split

melbourne_file_path = ""
melbourne_data = pd.read_csv(melbourne_file_path)

y = melbourne_data.Price
melbourne_predictors = ['Rooms']
X = melbourne_data[melbourne_predictors]

# split data into training and validation data, for both predictors and target
# The split is based on a random number generator. Supplying a numeric value to
# the random_state argument guarantees we get the same split every time we
# run this script.
train_X, val_X, train_y, val_y = train_test_split(X, y, random_state=0)

reference:

Sklearn-train_test_split随机划分训练集和测试集