用鸢尾花数据集复现一下朴素贝叶斯算法

要复现朴素贝叶斯算法使用鸢尾花数据集，您可以按照以下步骤进行操作：

1. 导入所需的库：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score

2. 加载鸢尾花数据集：

iris = load_iris()
X = iris.data
y = iris.target

3. 将数据集拆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)

4. 初始化朴素贝叶斯分类器并进行训练：

gnb = GaussianNB()
gnb.fit(X_train, y_train)

5. 使用训练好的模型进行预测：

y_pred = gnb.predict(X_test)

6. 计算准确率：

accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

完整的代码如下所示：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score

iris = load_iris()
X = iris.data
y = iris.target

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

gnb = GaussianNB()
gnb.fit(X_train, y_train)

y_pred = gnb.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)

以上代码将使用朴素贝叶斯算法对鸢尾花数据集进行分类，并输出准确率。