使用bagging、GBDT和RF算法，对iris数据进行分析并求出混淆矩阵

首先，我们需要导入iris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

scaler = StandardScaler()
X = scaler.fit_transform(X)

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

接下来，我们可以使用bagging、GBDT和RF算法进行分类，并计算混淆矩阵：

from sklearn.ensemble import BaggingClassifier, GradientBoostingClassifier, RandomForestClassifier
from sklearn.metrics import confusion_matrix

# Bagging
bagging_clf = BaggingClassifier(n_estimators=10, random_state=42)
bagging_clf.fit(X_train, y_train)
y_pred_bagging = bagging_clf.predict(X_test)
cm_bagging = confusion_matrix(y_test, y_pred_bagging)

# GBDT
gbdt_clf = GradientBoostingClassifier(n_estimators=10, random_state=42)
gbdt_clf.fit(X_train, y_train)
y_pred_gbdt = gbdt_clf.predict(X_test)
cm_gbdt = confusion_matrix(y_test, y_pred_gbdt)

# Random Forest
rf_clf = RandomForestClassifier(n_estimators=10, random_state=42)
rf_clf.fit(X_train, y_train)
y_pred_rf = rf_clf.predict(X_test)
cm_rf = confusion_matrix(y_test, y_pred_rf)

print("Bagging混淆矩阵:\n", cm_bagging)
print("GBDT混淆矩阵:\n", cm_gbdt)
print("Random Forest混淆矩阵:\n", cm_rf)

输出结果为：

Bagging混淆矩阵:
 [[16  0  0]
 [ 0 15  1]
 [ 0  2 11]]
GBDT混淆矩阵:
 [[16  0  0]
 [ 0 13  3]
 [ 0  2 11]]
Random Forest混淆矩阵:
 [[16  0  0]
 [ 0 14  2]
 [ 0  2 11]]