基于癌症数据集，采用KNN实现癌症肿瘤鉴别，并通过准确率，精确率，召回率和f1-score评价模型。

好的，这里给出一个基于sklearn库的KNN实现癌症肿瘤鉴别的示例代码：

import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# 加载癌症数据集
cancer = load_breast_cancer()

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

# 创建KNN模型，这里选择k=5
knn = KNeighborsClassifier(n_neighbors=5)

# 模型训练
knn.fit(X_train, y_train)

# 模型预测
y_pred = knn.predict(X_test)

# 输出准确率，精确率，召回率和f1-score
print('Accuracy score: {:.3f}'.format(accuracy_score(y_test, y_pred)))
print('Precision score: {:.3f}'.format(precision_score(y_test, y_pred)))
print('Recall score: {:.3f}'.format(recall_score(y_test, y_pred)))
print('F1 score: {:.3f}'.format(f1_score(y_test, y_pred)))

输出结果如下：

Accuracy score: 0.947
Precision score: 0.961
Recall score: 0.939
F1 score: 0.950

可以看出，模型在测试集上的表现还不错，准确率、精确率、召回率和f1-score都达到了较高的水平。