机器学习之sklearn"/>
机器学习之sklearn
(案例):用sklearn机器学习包简单实现KNN分类检测。
导包:
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import matplotlib.pyplot as plt
构造数据集:
1.特征值数据,癌变部位大小,时间
data_canner_feature=[
[3.393533211, 2.331273381],
[3.110073483, 1.781539638],
[1.343808831, 3.368360954],
[3.582294042, 4.679179110],
[2.280362439, 2.866990263],
[7.423436942, 4.696522875],
[5.745051997, 3.533989803],
[9.172168622, 2.511101045],
[7.792783481, 3.424088941],
[7.939820817, 0.791637231]]
2.癌症目标值数据0表示未癌变,1表示癌变
data_canner_target=[0, 0, 0, 0, 0, 1, 1, 1, 1, 1]
3.待预测样本
predict_data=[[8.093607318, 3.365731514]]
数据处理:
数据转换,列表转化为矩阵向量
X=np.array(data_canner_feature)
y=np.array(data_canner_target)
x=
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