Python 3

好的，我先导入必要的库和数据集，代码如下：

from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.datasets import fetch_openml

# 导入数据集
mnist = fetch_openml('mnist_784', version=1, cache=True)
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)

接下来，我分别使用随机森林分类器、Adaboost分类器和梯度提升树进行分类，并计算它们的准确率。代码如下：

# 随机森林分类器
rf_clf = RandomForestClassifier(n_estimators=100)
rf_clf.fit(X_train, y_train)
rf_y_pred = rf_clf.predict(X_test)
rf_accuracy = accuracy_score(y_test, rf_y_pred)
print("Random Forest Classifier accuracy:", rf_accuracy)

# Adaboost分类器
ada_clf = AdaBoostClassifier(n_estimators=100)
ada_clf.fit(X_train, y_train)
ada_y_pred = ada_clf.predict(X_test)
ada_accuracy = accuracy_score(y_test, ada_y_pred)
print("Adaboost Classifier accuracy:", ada_accuracy)

# 梯度提升树
gb_clf = GradientBoostingClassifier(n_estimators=100)
gb_clf.fit(X_train, y_train)
gb_y_pred = gb_clf.predict(X_test)
gb_accuracy = accuracy_score(y_test, gb_y_pred)
print("Gradient Boosting Classifier accuracy:", gb_accuracy)

执行以上代码后，会输出每个分类器的准确率。