导入库

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

加载鸢尾花数据集

iris = load_iris() x = iris['data'] y = iris['target']

将数据集分为训练集和测试集

np.random.seed(0) x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1)

任务1：创建 GaussianNB 对象

########## Begin ########## GNB = GaussianNB() ########## End ##########

任务2：调用 fit 函数执行训练过程

########## Begin ########## GNB.fit(x_train, y_train) ########## End ##########

调用 predict 函数进行预测

y_pred = GNB.predict(x_test)

计算模型准确率

acc = np.sum(y_pred==y_test)/y_test.size

打印结果

print("真实标签：\n", y_test) print("预测标签：\n", y_pred) print("模型准确率为：", acc