from sklearn.model selection import train _test_split

`from sklearn.model_selection import train_test_split`是一个用于将数据集分成训练集和测试集的函数。该函数可以将一个或多个数组作为输入，并根据指定的测试集大小或训练集大小将其拆分为两个不同的集合。以下是一个使用train_test_split函数的例子：

from sklearn.model_selection import train_test_split
import numpy as np

# 创建一个包含10个元素的数组
X = np.arange(10).reshape((5, 2))
y = range(5)

# 将数据集拆分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 输出拆分后的结果
print("X_train:\n", X_train)
print("X_test:\n", X_test)
print("y_train:\n", y_train)
print("y_test:\n", y_test)

该代码将一个包含10个元素的数组拆分为训练集和测试集，其中测试集大小为30%。输出结果将显示拆分后的训练集和测试集。

相关问题

from sklearn.datasets import ? from sklearn.model selection import train_test_split from sklearn.neural network import MLPClassifier import numpy as np digits = load_digits() X=digits.data y=digits.target X train X test,y_train,y_test = ? (X,y,random_state=0) mip = ? (solver ='lbfgs',hidden_layer_sizes=[100,100],activation='relu',random_state=62) mlp.fit(X_train,y_train) print(X_train.shape,y_train.shape,X_test.shape,y_test.shape) print("训练得分: {:.2f}".format(mlp.score(X_train.y_train))) print("测试得分: {:.2f}".format(mlp.score(X_test y_test)))

这段代码存在一些问题，可能会导致运行错误，我来帮你修改一下：

from sklearn.datasets import load_digits 
from sklearn.model_selection import train_test_split 
from sklearn.neural_network import MLPClassifier 
import numpy as np 

digits = load_digits() 
X = digits.data 
y = digits.target 

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) 

mlp = MLPClassifier(solver ='lbfgs', hidden_layer_sizes=[100,100], activation='relu', random_state=62) 

mlp.fit(X_train, y_train) 

print(X_train.shape, y_train.shape, X_test.shape, y_test.shape) 
print("训练得分: {:.2f}".format(mlp.score(X_train, y_train))) 
print("测试得分: {:.2f}".format(mlp.score(X_test, y_test)))

修改了以下几个地方：

1. `load_digits` 的拼写错误
2. `train_test_split` 的调用方式错误，应该为 `train_test_split(X, y, random_state=0)`
3. `MLPClassifier` 的调用方式错误，应该为 `MLPClassifier(solver='lbfgs', hidden_layer_sizes=[100,100], activation='relu', random_state=62)`
4. 打印得分时，应该为 `mlp.score(X_train, y_train)` 和 `mlp.score(X_test, y_test)`

from sklearn.preprocessing import StandardScaler from sklearn.decomposition import PCA from sklearn.model_selection import train_test_split from sklearn.model_selection import ShuffleSplit from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report

这是一段用于导入一些常见的机器学习相关的库和模块的代码。它导入了`StandardScaler`用于数据标准化，`PCA`用于数据降维，`train_test_split`用于数据集划分，`ShuffleSplit`用于交叉验证，`confusion_matrix`用于混淆矩阵计算，`classification_report`用于分类报告生成。这些库和模块都是来自于scikit-learn（sklearn）这个Python机器学习库。你有关这段代码的其他问题吗？