采用KNN，SVM，决策树、集成学习方法实现中鸢尾花数据集分类，挑选准确率最高的模型代码

好的，下面是使用 Python 实现中鸢尾花数据集分类的代码：

首先，导入所需的库和数据集：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier

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

接着，对数据集进行划分，设置训练集占比为 0.7：

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

然后，使用 KNN、SVM、决策树、随机森林等方法进行分类：

# KNN
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
knn_score = knn.score(X_test, y_test)

# SVM
svm = SVC()
svm.fit(X_train, y_train)
svm_score = svm.score(X_test, y_test)

# 决策树
dtc = DecisionTreeClassifier()
dtc.fit(X_train, y_train)
dtc_score = dtc.score(X_test, y_test)

# 随机森林
rfc = RandomForestClassifier(n_estimators=10)
rfc.fit(X_train, y_train)
rfc_score = rfc.score(X_test, y_test)

最后，比较各个方法的准确率，选择准确率最高的方法作为最终的分类器：

scores = [knn_score, svm_score, dtc_score, rfc_score]
methods = ['KNN', 'SVM', 'Decision Tree', 'Random Forest']
best_score = max(scores)
best_method = methods[scores.index(best_score)]
print(f"The best method is {best_method} with a score of {best_score:.2f}")

完整代码如下：

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier

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

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# KNN
knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
knn_score = knn.score(X_test, y_test)

# SVM
svm = SVC()
svm.fit(X_train, y_train)
svm_score = svm.score(X_test, y_test)

# 决策树
dtc = DecisionTreeClassifier()
dtc.fit(X_train, y_train)
dtc_score = dtc.score(X_test, y_test)

# 随机森林
rfc = RandomForestClassifier(n_estimators=10)
rfc.fit(X_train, y_train)
rfc_score = rfc.score(X_test, y_test)

scores = [knn_score, svm_score, dtc_score, rfc_score]
methods = ['KNN', 'SVM', 'Decision Tree', 'Random Forest']
best_score = max(scores)
best_method = methods[scores.index(best_score)]
print(f"The best method is {best_method} with a score of {best_score:.2f}")

希望能对您有所帮助！