生成利用KNN算法对数据集进行二分类的代码

首先需要导入必要的库：

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

然后，我们可以准备数据集。这里我们使用 sklearn 自带的 iris 数据集。

from sklearn.datasets import load_iris

iris = load_iris()
X = iris.data
y = iris.target

接下来，我们将数据集分成训练集和测试集。我们将训练集占总数据集的 80%，测试集占 20%。

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

接下来，我们需要创建一个 KNN 分类器，并对训练集进行拟合。

k = 3
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, y_train)

现在，我们已经训练好了我们的分类器，可以使用测试集进行预测并计算准确率。

y_pred = knn.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")

完整代码如下：

import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.datasets import load_iris

iris = load_iris()
X = iris.data
y = iris.target

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

k = 3
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, y_train)

y_pred = knn.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")