实现乳腺癌数据集内容的KNN算法，要求使用交叉验证算法，尽量求出更好的值与更好的算法。

首先，需要导入所需的库和数据集：

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score

data = pd.read_csv('breast_cancer.csv')

接着，我们需要对数据集进行预处理，包括将分类变量转换为数字变量、删除无用的列等等：

# 将分类变量转换为数字变量
data['diagnosis'] = data['diagnosis'].map({'M':1, 'B':0})

# 删除无用的列
data = data.drop(['id', 'Unnamed: 32'], axis=1)

# 将数据集分为特征和目标
X = data.drop(['diagnosis'], axis=1).values
y = data['diagnosis'].values

然后，我们将数据集分为训练集和测试集，以便进行模型拟合和评估：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

接下来，我们使用交叉验证算法来确定最佳的K值：

# 定义K值的范围
k_range = range(1, 31)

# 用来保存每个K值对应的交叉验证得分
k_scores = []

# 对于每个K值，进行交叉验证并计算得分
for k in k_range:
    knn = KNeighborsClassifier(n_neighbors=k)
    scores = cross_val_score(knn, X_train, y_train, cv=10, scoring='accuracy')
    k_scores.append(scores.mean())

# 找到最佳K值
best_k = k_range[k_scores.index(max(k_scores))]
print("Best K value:", best_k)

最后，我们使用最佳的K值来拟合模型并进行预测：

# 拟合模型
knn = KNeighborsClassifier(n_neighbors=best_k)
knn.fit(X_train, y_train)

# 进行预测
y_pred = knn.predict(X_test)

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

完整代码如下：

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import cross_val_score

data = pd.read_csv('breast_cancer.csv')

# 将分类变量转换为数字变量
data['diagnosis'] = data['diagnosis'].map({'M':1, 'B':0})

# 删除无用的列
data = data.drop(['id', 'Unnamed: 32'], axis=1)

# 将数据集分为特征和目标
X = data.drop(['diagnosis'], axis=1).values
y = data['diagnosis'].values

# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义K值的范围
k_range = range(1, 31)

# 用来保存每个K值对应的交叉验证得分
k_scores = []

# 对于每个K值，进行交叉验证并计算得分
for k in k_range:
    knn = KNeighborsClassifier(n_neighbors=k)
    scores = cross_val_score(knn, X_train, y_train, cv=10, scoring='accuracy')
    k_scores.append(scores.mean())

# 找到最佳K值
best_k = k_range[k_scores.index(max(k_scores))]
print("Best K value:", best_k)

# 拟合模型
knn = KNeighborsClassifier(n_neighbors=best_k)
knn.fit(X_train, y_train)

# 进行预测
y_pred = knn.predict(X_test)

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