python实现贝叶斯判别

贝叶斯判别是一种分类算法，它基于贝叶斯定理和特征的先验概率，通过对后验概率进行计算，从而确定样本的分类。

在Python中，可以使用sklearn库中的朴素贝叶斯算法实现贝叶斯判别。具体步骤如下：

1. 导入需要的库和数据集

from sklearn.datasets import load_iris
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split

2. 加载iris数据集

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

3. 将数据集分为训练集和测试集

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)

4. 创建分类器对象

clf = GaussianNB()

5. 在训练集上训练分类器

clf.fit(X_train, y_train)

6. 在测试集上进行预测

y_pred = clf.predict(X_test)

7. 计算分类器的准确率

from sklearn.metrics import accuracy_score
accuracy_score(y_test, y_pred)

完整代码如下：

from sklearn.datasets import load_iris
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
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=0.3, random_state=0)

clf = GaussianNB()

clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)

accuracy_score(y_test, y_pred)