Python代码，良性恶性肿瘤数据，直接从sklearn导入如下 from sklearn.datasets import load_breast_cancer cancer = load_breast_cancer() 划分测试与训练集，用logistic回归完成分类任务

好的，以下是Python代码实现：

# 导入模块
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# 加载数据集
cancer = load_breast_cancer()

# 划分训练集和测试集，将数据集划分为7:3
X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, test_size=0.3, random_state=0)

# 初始化逻辑回归模型
logistic = LogisticRegression()

# 训练模型
logistic.fit(X_train, y_train)

# 预测结果
y_pred = logistic.predict(X_test)

# 计算准确率
accuracy = accuracy_score(y_test, y_pred)

# 输出准确率
print('Accuracy:', accuracy)

这段代码首先导入了必要的模块，加载了sklearn的乳腺癌数据集，然后将数据集划分为训练集和测试集，使用逻辑回归模型进行训练和预测，并计算了准确率。最后输出了准确率。