X_train, X_test, y_train, y_test = train_test_split(data, result, test_size=0.1, random_state=110)如何不设定随机种子
时间: 2023-11-02 20:02:29 浏览: 38
如果你不想设定随机种子,可以将参数 random_state 设定为 None。这样做会使 train_test_split 函数使用默认的随机种子,即每次运行时都会产生不同的随机结果。你可以这样修改代码:
```python
X_train, X_test, y_train, y_test = train_test_split(data, result, test_size=0.1, random_state=None)
```
相关问题
train_test_split(x,y,test_size=0.3)
train_test_split(x, y, test_size=0.3)是一种常用的机器学习方法,它用于将原始数据集按照一定比例分割成训练集和测试集。其中,x表示特征数据集,y表示目标变量集。test_size=0.3表示将数据集按照30%的比例分割为测试集,剩余的70%用作训练集。
以下是train_test_split函数的使用示例:
import numpy as np
from sklearn.model_selection import train_test_split
X, y = np.arange(10).reshape((5, 2)), range(5)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
其中,X_train和y_train表示训练集的特征数据和目标变量,X_test和y_test表示测试集的特征数据和目标变量。
这种分割方法的作用是为了评估机器学习模型的性能。通过将数据集划分为训练集和测试集,我们可以在训练集上训练模型,并在测试集上评估模型的性能,从而得到对模型泛化能力的估计。同时,通过设定test_size参数,我们可以控制测试集的大小,以满足实际需求。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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x_train, x_test, y_train, y_test = train_test_split(x_data,y_data, test_size=0.3, random_state=33)
train_test_split()函数用于分割数据集,将给定数据集分为训集和测试集。根引用的描述train_test_split()函数的参数包括Xy(特征和目标变量)、_size(测试集大小)、random_state(随机种子)、shuffle(是否对数据进行洗牌)和stratify(是否按照y的类别进行分层抽样)。
根据您提供的代码,x_data和y_data是输入的特征和目标变量,而test_size=0.3表示测试集占总数据的30%,random_state=33表示使用固定的随机种子33来保证每次运行的结果一致。
因此,根据给定的代码,x_train、x_test、y_train和y_test的赋值可以表示为 x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size=0.3, random_state=33)。这将根据指定的参数将输入数据集进行分割,并将分割后的结果分别赋值给x_train、x_test、y_train和y_test。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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