train_test_split()函数 各个参数的作用
时间: 2024-04-04 16:28:17 浏览: 14
train_test_split()函数是机器学习中常用的用于将数据集划分为训练集和测试集的函数。它的作用是将原始数据集按照一定比例划分为训练集和测试集,以便进行模型的训练和评估。
train_test_split()函数的参数及其作用如下:
1. test_size:指定测试集的大小或比例。可以是一个浮点数(0到1之间),表示测试集占总样本的比例;也可以是一个整数,表示测试集的样本数量。默认值为0.25。
2. train_size:指定训练集的大小或比例。可以是一个浮点数(0到1之间),表示训练集占总样本的比例;也可以是一个整数,表示训练集的样本数量。默认值为None,表示剩余的样本全部作为训练集。
3. random_state:指定随机数种子,用于控制数据集的随机划分。设置相同的种子可以保证每次划分得到的结果一致。默认值为None,表示使用当前时间戳作为种子。
4. shuffle:指定是否在划分前对数据进行洗牌。如果设置为True,则在划分前会将数据打乱顺序;如果设置为False,则按照原始顺序划分数据。默认值为True。
5. stratify:指定是否根据标签的分布进行分层抽样。如果设置为非空的数组或Series,划分的训练集和测试集中的类别比例会与原始数据集中的类别比例相同。默认值为None,表示不进行分层抽样。
相关问题
train_test_split 函数作用
train_test_split函数是用于划分数据集的函数,常用于机器学习和深度学习算法中。它可以将数据集按照一定的比例划分为训练集和测试集,以便进行模型的训练和评估。
train_test_split函数的作用是将输入的数据集划分为训练集和测试集,并返回划分后的数据。它可以接受多个输入参数,包括特征数据、目标数据和划分比例等。通过指定划分比例,可以控制训练集和测试集的大小。
使用train_test_split函数可以帮助我们在机器学习算法中进行数据集的划分,从而更好地评估模型的性能和泛化能力。
范例:<<引用:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[10]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[11]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[12]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[13]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[14]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[15]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[16]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[17]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[18]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[19]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[20]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[21]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[22]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[23]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[24]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[25]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[26]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[27]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[28]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[29]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[30]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[31]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[32]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[33]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[34]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[35]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[36]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[37]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[38]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[39]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[40]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[41]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[42]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[43]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[44]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[45]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[46]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[47]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)。引用[48]:from sklearn.model_selection import train_test_split X, y = load_iris(return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
train_test_split函数参数
train_test_split函数是Python中用于划分训练集和测试集的函数。它的参数包括:
- `arrays`:需要划分的数据集,可以是一个数组或多个数组的列表。
- `test_size`:测试集的大小,可以是一个浮点数(表示比例)或一个整数(表示样本数量)。
- `train_size`:训练集的大小,可以是一个浮点数(表示比例)或一个整数(表示样本数量)。
- `random_state`:随机种子,用于控制数据集的随机划分。
- `shuffle`:是否在划分之前对数据进行洗牌,默认为True。
- `stratify`:是否根据标签进行分层划分,默认为None。
下面是一个示例:
```python
from sklearn.model_selection import train_test_split
X = [1, 2, 3, 4, 5]
y = [0, 1, 0, 1, 0]
X_train, X_test, y_train, y = train_test_split(X, y, test_size=0.2, random_state=42)
```
这个示例将X和y划分为训练集和测试集,其中测试集占总样本的20%。划分后的结果存储在X_train、X_test、y_train和y_test中。