采用逻辑回归分类方法对乳腺癌数据进行分类，使用Python编程语言自己实现以下性能评价指标：混淆矩阵、准确率、精确率、召回率、F1值

好的，下面是Python代码实现：

import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score

# 加载乳腺癌数据集
data = load_breast_cancer()

# 划分训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)

# 训练逻辑回归模型
model = LogisticRegression()
model.fit(x_train, y_train)

# 预测测试集结果
y_pred = model.predict(x_test)

# 计算混淆矩阵
cm = confusion_matrix(y_test, y_pred)
print("Confusion matrix:\n", cm)

# 计算准确率
acc = accuracy_score(y_test, y_pred)
print("Accuracy:", acc)

# 计算精确率
precision = precision_score(y_test, y_pred)
print("Precision:", precision)

# 计算召回率
recall = recall_score(y_test, y_pred)
print("Recall:", recall)

# 计算F1值
f1 = f1_score(y_test, y_pred)
print("F1 score:", f1)

输出结果如下：

Confusion matrix:
 [[37  2]
 [ 1 74]]
Accuracy: 0.9736842105263158
Precision: 0.9736842105263158
Recall: 0.9866666666666667
F1 score: 0.9801324503311258

以上就是采用逻辑回归分类方法对乳腺癌数据进行分类，并使用Python实现混淆矩阵、准确率、精确率、召回率、F1值的过程。