以下是几种不同算法实现鸢尾花多分类识别的 Python 代码： ### 逻辑回归实现 ```python from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score # 加载鸢尾花数据集 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) # 创建逻辑回归模型 model = LogisticRegression() # 训练模型 model.fit(X_train, y_train) # 进行预测 y_pred = model.predict(X_test) # 计算准确率 accuracy = accuracy_score(y_test, y_pred) print(f"逻辑回归模型准确率: {accuracy}") ``` ### KNN 算法实现 ```python 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 # 加载鸢尾花数据集 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 模型 k = 3 knn = KNeighborsClassifier(n_neighbors=k) # 训练模型 knn.fit(X_train, y_train) # 进行预测 y_pred = knn.predict(X_test) # 计算准确率 accuracy = accuracy_score(y_test, y_pred) print(f"KNN 模型准确率: {accuracy}") ``` ### 决策树实现 ```python from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score # 加载鸢尾花数据集 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) # 创建决策树模型 dt = DecisionTreeClassifier() # 训练模型 dt.fit(X_train, y_train) # 进行预测 y_pred = dt.predict(X_test) # 计算准确率 accuracy = accuracy_score(y_test, y_pred) print(f"决策树模型准确率: {accuracy}") ```