minist朴素贝叶斯实现

朴素贝叶斯（Naive Bayes）是一种基于贝叶斯定理和特征条件独立假设的分类算法。在文本分类、垃圾邮件过滤、情感分析等领域都有广泛应用。下面是一个使用朴素贝叶斯对MNIST数据集进行分类的示例代码。

首先，我们需要导入相关的库和数据集：

from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score

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)

接下来，我们使用`MultinomialNB`类来创建一个朴素贝叶斯分类器，并使用训练数据对其进行训练：

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

训练完成后，我们可以使用测试数据对分类器进行评估：

y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

最终输出的准确率为：

Accuracy: 0.8322142857142857

完整代码如下：

from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import accuracy_score

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)

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

y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)