熟悉sklearn中的集成方法的使用,使用mnist数据(访问方法见下面的cell),重复上述实验,实验中调节各模型的参数据应使得各模型的得分尽量高,

好的，针对您的问题，我可以给出以下步骤：

1. 导入所需库和数据集：

from sklearn.datasets import fetch_openml
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier, BaggingClassifier, AdaBoostClassifier, GradientBoostingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import numpy as np

mnist = fetch_openml('mnist_784')
X, y = mnist["data"], mnist["target"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

2. 分别使用不同的集成方法构建模型：

# 随机森林
rf_clf = RandomForestClassifier(n_estimators=100, random_state=42)
rf_clf.fit(X_train, y_train)
rf_pred = rf_clf.predict(X_test)
rf_score = accuracy_score(y_test, rf_pred)
print("Random Forest Accuracy: ", rf_score)

# Bagging
bag_clf = BaggingClassifier(DecisionTreeClassifier(max_depth=5), n_estimators=100, max_samples=100, bootstrap=True, random_state=42)
bag_clf.fit(X_train, y_train)
bag_pred = bag_clf.predict(X_test)
bag_score = accuracy_score(y_test, bag_pred)
print("Bagging Accuracy: ", bag_score)

# AdaBoost
ada_clf = AdaBoostClassifier(DecisionTreeClassifier(max_depth=5), n_estimators=100, algorithm="SAMME.R", learning_rate=0.5, random_state=42)
ada_clf.fit(X_train, y_train)
ada_pred = ada_clf.predict(X_test)
ada_score = accuracy_score(y_test, ada_pred)
print("AdaBoost Accuracy: ", ada_score)

# Gradient Boosting
gb_clf = GradientBoostingClassifier(max_depth=5, n_estimators=100, learning_rate=0.5, random_state=42)
gb_clf.fit(X_train, y_train)
gb_pred = gb_clf.predict(X_test)
gb_score = accuracy_score(y_test, gb_pred)
print("Gradient Boosting Accuracy: ", gb_score)

3. 分别输出各个集成模型的准确率，调节各模型的参数，使得各模型的得分尽量高。

总体来说，这些集成方法可以提高模型的准确率，但是需要注意的是，随着集成模型的增多，训练时间也会相应增加，因此需要在准确率和训练时间之间做出权衡。同时，调参也是一个比较关键的过程，需要根据具体情况进行调整。