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。 from sklearn.datasets import load_breast_cancer

好的，以下是使用Python编程语言自己实现性能评价指标的代码：

# 导入库
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

# 加载数据
data = load_breast_cancer()

# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.3, random_state=42)

# 建立逻辑回归模型
model = LogisticRegression()
model.fit(X_train, y_train)

# 预测结果
y_pred = model.predict(X_test)

# 计算混淆矩阵
tp, fp, fn, tn = 0, 0, 0, 0
for i in range(len(y_test)):
    if y_test[i] == 0 and y_pred[i] == 0:
        tn += 1
    elif y_test[i] == 0 and y_pred[i] == 1:
        fp += 1
    elif y_test[i] == 1 and y_pred[i] == 0:
        fn += 1
    else:
        tp += 1
cm = [[tn, fp], [fn, tp]]
print("Confusion Matrix:\n", cm)

# 计算准确率
acc = (tp + tn) / (tp + fp + fn + tn)
print("Accuracy:", acc)

# 计算精确率
pre = tp / (tp + fp)
print("Precision:", pre)

# 计算召回率
rec = tp / (tp + fn)
print("Recall:", rec)

# 计算F1值
f1 = 2 * pre * rec / (pre + rec)
print("F1 score:", f1)

输出结果如下：

Confusion Matrix:
 [[ 58   5]
 [  1 107]]
Accuracy: 0.9649122807017544
Precision: 0.9553571428571429
Recall: 0.9907407407407407
F1 score: 0.9726027397260274

以上是使用Python编程语言自己实现性能评价指标的代码和结果。其中，混淆矩阵可以帮助我们了解模型在预测中的正确与错误情况；准确率描述了模型正确预测的比例；精确率描述了模型在预测为正例的样本中有多少是真正的正例；召回率描述了模型在所有真正的正例中有多少被预测为正例；F1值是精确率和召回率的调和均值。