使用朴素贝叶斯的类特定的属性值加权python实现breast-cancer数据集输出结果是怎样的

以下是使用朴素贝叶斯的类特定的属性值加权Python实现Breast-Cancer数据集的示例代码：

import numpy as np
from sklearn import datasets
from sklearn.naive_bayes import GaussianNB

# 加载Breast-Cancer数据集
breast_cancer = datasets.load_breast_cancer()

# 取出数据和目标变量
X = breast_cancer.data
y = breast_cancer.target

# 通过计算每个类的属性值的均值和标准差来计算每个属性的权重
weights = []
for i in range(X.shape[1]):
    mean_0 = np.mean(X[y == 0, i])
    std_0 = np.std(X[y == 0, i])
    mean_1 = np.mean(X[y == 1, i])
    std_1 = np.std(X[y == 1, i])
    weight = (mean_1 - mean_0) / (std_0 + std_1)
    weights.append(weight)

# 对每个样本的属性进行加权
X_weighted = X * np.array(weights)

# 使用朴素贝叶斯分类器进行分类
gnb = GaussianNB()
gnb.fit(X_weighted, y)
y_pred = gnb.predict(X_weighted)

# 输出分类结果
print("Classification accuracy: {:.2f}%".format(np.mean(y_pred == y) * 100))

输出结果可能因为随机性而有所不同，但是分类准确率应该在90%以上