[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$frJLc2HBCsuirA4HcbjrlOh9iuOpijfdKVjVIanVh0Ts":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":10,"question":14,"related":15,"source":25,"type":54},[],"2023-10-27 23:46:03",102020576,[8,9],"正确","错误",{"courseId":11,"courseImg":12,"courseName":13},"35106a2c65ca6ca9f6a0ec3652c01c78","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002Fe3c5ea4224391187d23a3344cf83ab48.jpg","机器学习","判断聚类和分类的区别在于用于聚类的训练样本的类标记是未知的",[16,27,36,45,51,55,60],{"answer":17,"createTime":5,"id":18,"options":19,"question":24,"source":25,"type":26},[],102020569,[20,21,22,23],"给定一个关于用户信息的数据库,自动将用户分组到不同的市场细分中","根据历史天气记录,预测明天的降雨量","给定超市中大量产品的销售数据,估计这些产品的未来销售额","基于许多电子邮件,确定它们是垃圾邮件还是非垃圾邮件","对于以下哪项任务,K-means聚类可能是一种合适的算法()","v1",0,{"answer":28,"createTime":5,"id":29,"options":30,"question":35,"source":25,"type":26},[],102020570,[31,32,33,34],"K-Means算法的主要缺点之一为K值很难确定","K值决定了初始质心的数量","K 值需要人为设定,不同 K 值得到的结果一样","常见的选取 K 值的方法有:手肘法、轮廓系数法","下列关于K值说法不正确的是()",{"answer":37,"createTime":5,"id":38,"options":39,"question":44,"source":25,"type":26},[],102020572,[40,41,42,43],"KNN和K-means都属于监督学习","KNN和K-means都需要带标签的数据集","KNN和K-means都是聚类算法","KNN和K-means都需要使用到距离度量","K-means算法和KNN算法相比较,下列说法正确的是()",{"answer":46,"createTime":5,"id":47,"options":48,"question":49,"source":25,"type":50},[],102020573,[],"聚类试图将数据集中的样本划分为若干个通常是不相交的子集,每个子集称为一个&quot;( )&quot;",2,{"answer":52,"createTime":5,"id":6,"options":53,"question":14,"source":25,"type":54},[],[8,9],3,{"answer":56,"createTime":5,"id":57,"options":58,"question":59,"source":25,"type":54},[],102020578,[8,9],"聚类生成的组称为簇,簇内任意对象之间具有较高的相似度,而簇间任意对象之间具有较高的相异度",{"answer":61,"createTime":5,"id":62,"options":63,"question":64,"source":25,"type":54},[],102020579,[8,9],"直观上看,我们希望&quot;物以类聚&quot;,即聚类的结果&quot;簇内相似度&quot;高,且&quot;簇间&quot;相似度也高"]