朴素贝叶斯

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

if __name__ == '__main__':
    dataset = load_iris()
    X = dataset.data
    y = dataset.target

    # 将数组或矩阵拆分为 随机训练 和 测试子集
    Xd_train, Xd_test, y_train, y_test = train_test_split(X, y, random_state=14)

    # 调用贝叶斯算法
    clf = GaussianNB()

    # fit 根据 X, y 拟合高斯朴素贝叶斯
    clf = clf.fit(Xd_train, y_train)
    # 预测（测试数据）
    y_predicted = clf.predict(Xd_test)

    # 预测准确率
    accuracy = np.mean(y_predicted == y_test) * 100

    print("y_test     ", y_test)
    print("y_predicted", y_predicted)
    print("accuracy:", accuracy)