1.K-近邻算法API
- sklearn.neighbors.KNeighborsClassifier(n_neighbors=5,algorithm='auto')
- n_neighbors:
- algorithm:{‘auto’,‘ball_tree’,‘kd_tree’,‘brute’}
2.数据集介绍
3.步骤分析
- 1.获取数据集
- 2.数据基本处理
- 3.特征工程
- 4.机器学习(模型训练)
- 5.模型评估
4.代码
- 导入模块
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
- 先从sklearn当中获取数据集,然后进行数据集的分割
# 1.获取数据集
iris = load_iris()
# 2.数据基本处理
# x_train,x_test,y_train,y_test为训练集特征值、测试集特征值、训练集目标值、测试集目标值
x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=22)
- 进行数据标准化
- 特征值的标准化
# 3、特征工程:标准化
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
- 模型进行训练预测
# 4、机器学习(模型训练)
estimator = KNeighborsClassifier(n_neighbors=9)
estimator.fit(x_train, y_train)
# 5、模型评估
# 方法1:比对真实值和预测值
y_predict = estimator.predict(x_test)
print("预测结果为:\n", y_predict)
print("比对真实值和预测值:\n", y_predict == y_test)
# 方法2:直接计算准确率
score = estimator.score(x_test, y_test)
print("准确率为:\n", score)
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