利用scikit-learn、Python语言编程实现K近邻算法.评估算法运行结果，并进行优化，可视化显示

在Python中，利用scikit-learn库可以轻松地实现K近邻(K-Nearest Neighbors, KNN)算法。下面是基本步骤：

1. **导入所需库**：

import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score, confusion_matrix

2. **加载数据集**（这里假设用的是iris数据集为例）：

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

3. **数据预处理**，分割训练集和测试集：

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

4. **创建并训练KNN分类器**，例如选择k=3作为邻居数：

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

5. **预测测试集结果**：

y_pred = knn.predict(X_test)

6. **评估模型性能**：

accuracy = accuracy_score(y_test, y_pred)
conf_mat = confusion_matrix(y_test, y_pred)
print("Accuracy:", accuracy)
print("Confusion Matrix:\n", conf_mat)

7. **优化和调整参数**（如尝试不同的k值，网格搜索法等）：

from sklearn.model_selection import GridSearchCV
param_grid = {'n_neighbors': range(1, 31)}
grid_search = GridSearchCV(KNeighborsClassifier(), param_grid, cv=5)
grid_search.fit(X_train, y_train)
best_k = grid_search.best_params_['n_neighbors']
print("Best k:", best_k)

8. **可视化结果**，比如决策边界（仅适用于二分类问题）：

if y_train.shape[1] == 2:  # 假设是二分类问题
    h = .02  # step size in the mesh
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
    Z = grid_search.predict(np.c_[xx.ravel(), yy.ravel()])
    Z = Z.reshape(xx.shape)

    plt.figure()
    plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)
    plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', cmap=plt.cm.Paired)
    plt.xlabel('Feature 1')
    plt.ylabel('Feature 2')
    plt.title(f'Optimized KNN with k={best_k}')
    plt.show()