用决策树实现mnist数据集python

好的，我可以给您提供一些实现决策树对 MNIST 数据集的 Python 代码。

首先，导入必要的库：

import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

然后，加载 MNIST 数据集：

digits = load_digits()
X = digits.data
y = digits.target

接着，将数据集划分为训练集和测试集：

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

然后，定义并训练决策树模型：

clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)

最后，使用训练好的模型对测试集进行预测，并计算准确率：

y_pred = clf.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print("Accuracy:", acc)

完整代码如下：

import numpy as np
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

digits = load_digits()
X = digits.data
y = digits.target

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

clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print("Accuracy:", acc)

希望这个代码能够对您有所帮助！