编辑代码

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

# 加载数据
iris = load_iris()
X = iris.data
y = iris.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)

# 创建KNN分类器
knn = KNeighborsClassifier(n_neighbors=3)

# 训练模型
knn.fit(X_train, y_train)

# 预测
y_pred = knn.predict(X_test)

# 评估
print("预测结果:", y_pred)
print("真实标签:", y_test)
print("准确率:", accuracy_score(y_test, y_pred))








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

# 加载数据
iris = load_iris()
X = iris.data  # 4个特征的数据
y = iris.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)

# 创建KNN分类器
knn = KNeighborsClassifier(n_neighbors=3)

# 训练模型 - 使用原始形状的数据
knn.fit(X_train, y_train)

# 预测 - 使用原始形状的测试数据
y_pred = knn.predict(X_test)

# 评估
print("预测结果:", y_pred)
print("真实标签:", y_test)
print("准确率:", accuracy_score(y_test, y_pred))