from sklearn.model_selection import train_test_split X_train, X_test, Y_train, Y_test = train_test_split = (cancer.data, cancer.target,stratify = cancer.traget,random_state = 66) from sklearn.preprocession import StandardSscaler nn = StandardScaler() X_train = nn.fit_transform(X_test) X_test = nn.transform(X_test) 修改后的代码

这段代码有一些错误，我来帮你修改一下：

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_breast_cancer

cancer = load_breast_cancer()

X_train, X_test, y_train, y_test = train_test_split(
    cancer.data, cancer.target, stratify=cancer.target, random_state=66)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

修改后的代码做了以下几个修改：

1. 增加了导入 `load_breast_cancer` 数据集的代码
2. 将 `train_test_split` 函数的赋值符号改为函数调用
3. 修正了 `stratify` 参数的拼写错误
4. 将 `Y_train` 和 `Y_test` 改为小写字母，并且和数据集的标签对应
5. 将 `StandardSscaler` 改为 `StandardScaler`
6. 增加了 `scaler` 变量来存储 `StandardScaler` 类的实例，并且使用 `fit_transform` 和 `transform` 方法对训练集和测试集进行了标准化处理