机器学*-K*邻算法模型预测实战

一、数据准备

 二、任务目的

根据前三列数据预测最后一列的target数据

三、实现代码

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier

# 获取数据
iris = load_iris()
# 数据基本处理
x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2,)

# 特征工程-特征预处理
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.fit_transform(x_test)

# 机器学*-KNN
# 实例化一个估计器
estimator = KNeighborsClassifier(n_neighbors=5)
# 模型训练
estimator.fit(x_train, y_train)
# 模型评估
y_per = estimator.predict(x_test)

print("预测值是：
", y_per)
print("预测值和真实值的对比是：
", y_per == y_test)
# 准确率计算
score = estimator.score(x_test, y_test)
print("准确率为：
", score)

四、运行结果