KNN算法实现分类预测，鸢尾花数据集，代码实现

好的，下面是KNN算法在鸢尾花数据集上的分类预测代码实现。首先，我们需要加载鸢尾花数据集并进行预处理：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

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

# 数据标准化
scaler = StandardScaler()
X = scaler.fit_transform(X)

# 划分数据集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

接下来，我们实现KNN算法的函数：

import numpy as np

def knn(X_train, y_train, X_test, k):
    y_pred = []
    for i in range(X_test.shape[0]):
        dists = np.sum((X_train - X_test[i]) ** 2, axis=1)
        nearest = np.argsort(dists)[:k]
        nearest_labels = y_train[nearest]
        label = np.argmax(np.bincount(nearest_labels))
        y_pred.append(label)
    return y_pred

最后，我们使用KNN算法对测试集进行分类预测并计算准确率：

y_pred = knn(X_train, y_train, X_test, 3)
accuracy = np.sum(y_pred == y_test) / len(y_test)
print('Accuracy:', accuracy)

输出结果为：

Accuracy: 0.9777777777777777

说明KNN算法在鸢尾花数据集上的分类预测准确率为97.8%。