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，这里提供一个基于sklearn库实现KNN算法的示例代码，用于对癌症数据集进行肿瘤鉴别，并计算模型的准确率、精确率、召回率和f1-score：

# 导入库
from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# 加载数据集
data = load_breast_cancer()
X = data.data
y = data.target

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 定义KNN模型
knn = KNeighborsClassifier()

# 训练模型
knn.fit(X_train, y_train)

# 预测结果
y_pred = knn.predict(X_test)

# 计算模型评价指标
acc = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)

# 输出结果
print("Accuracy:", acc)
print("Precision:", precision)
print("Recall:", recall)
print("F1-score:", f1)

你可以根据自己的数据集进行修改和调整，希望对你有帮助。