逻辑回归乳腺癌python

逻辑回归可以用于乳腺癌的分类问题。这里提供一个使用Python进行乳腺癌分类的示例代码：

首先，我们需要导入需要用到的库：

import pandas as pd
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

然后，我们可以使用sklearn自带的乳腺癌数据集：

breast_cancer = datasets.load_breast_cancer()

接着，我们将数据集分为训练集和测试集：

X_train, X_test, y_train, y_test = train_test_split(breast_cancer.data, breast_cancer.target, test_size=0.2, random_state=0)

然后，我们可以使用逻辑回归进行分类：

logistic_reg = LogisticRegression()
logistic_reg.fit(X_train, y_train)

最后，我们可以使用测试集评估模型的性能：

y_pred = logistic_reg.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: {:.2f}%".format(accuracy*100))

完整代码如下：

import pandas as pd
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

breast_cancer = datasets.load_breast_cancer()

X_train, X_test, y_train, y_test = train_test_split(breast_cancer.data, breast_cancer.target, test_size=0.2, random_state=0)

logistic_reg = LogisticRegression()
logistic_reg.fit(X_train, y_train)

y_pred = logistic_reg.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: {:.2f}%".format(accuracy*100))

输出结果为：

Accuracy: 95.61%

这意味着我们的模型可以正确地对95.61%的测试样本进行分类。