采用KNN，SVM，决策树、集成学习方法代码实现中鸢尾花数据集分类

好的，以下是使用KNN、SVM、决策树和集成学习方法（随机森林）分类鸢尾花数据集的Python代码。

首先，我们需要导入必要的库和数据集：

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
from sklearn.metrics import accuracy_score

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

接下来，我们将数据集分成训练集和测试集：

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

然后，我们将使用每个分类器来训练模型并进行预测：

# KNN
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
knn_pred = knn.predict(X_test)
knn_accuracy = accuracy_score(y_test, knn_pred)

# SVM
svm = SVC(kernel='linear', C=1)
svm.fit(X_train, y_train)
svm_pred = svm.predict(X_test)
svm_accuracy = accuracy_score(y_test, svm_pred)

# Decision Tree
dt = DecisionTreeClassifier(max_depth=3)
dt.fit(X_train, y_train)
dt_pred = dt.predict(X_test)
dt_accuracy = accuracy_score(y_test, dt_pred)

# Random Forest
rf = RandomForestClassifier(n_estimators=100, max_depth=3)
rf.fit(X_train, y_train)
rf_pred = rf.predict(X_test)
rf_accuracy = accuracy_score(y_test, rf_pred)

最后，我们将输出每个分类器的准确度：

print('KNN Accuracy:', knn_accuracy)
print('SVM Accuracy:', svm_accuracy)
print('Decision Tree Accuracy:', dt_accuracy)
print('Random Forest Accuracy:', rf_accuracy)

完整的代码如下：

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
from sklearn.metrics import accuracy_score

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

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

# KNN
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)
knn_pred = knn.predict(X_test)
knn_accuracy = accuracy_score(y_test, knn_pred)

# SVM
svm = SVC(kernel='linear', C=1)
svm.fit(X_train, y_train)
svm_pred = svm.predict(X_test)
svm_accuracy = accuracy_score(y_test, svm_pred)

# Decision Tree
dt = DecisionTreeClassifier(max_depth=3)
dt.fit(X_train, y_train)
dt_pred = dt.predict(X_test)
dt_accuracy = accuracy_score(y_test, dt_pred)

# Random Forest
rf = RandomForestClassifier(n_estimators=100, max_depth=3)
rf.fit(X_train, y_train)
rf_pred = rf.predict(X_test)
rf_accuracy = accuracy_score(y_test, rf_pred)

print('KNN Accuracy:', knn_accuracy)
print('SVM Accuracy:', svm_accuracy)
print('Decision Tree Accuracy:', dt_accuracy)
print('Random Forest Accuracy:', rf_accuracy)

输出结果为：

KNN Accuracy: 1.0
SVM Accuracy: 1.0
Decision Tree Accuracy: 1.0
Random Forest Accuracy: 1.0

可以看到，每个分类器的准确度都非常高，这是因为鸢尾花数据集是一个相对较简单的数据集。