[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fshlReOF5f7xkqV9gd_vUnSxsgfPLtPyL8SW_-3wYm_0":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":16,"related":17,"source":27,"type":28},[],"2023-10-27 23:46:03",102020570,[8,9,10,11],"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",{"courseId":13,"courseImg":14,"courseName":15},"35106a2c65ca6ca9f6a0ec3652c01c78","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002Fe3c5ea4224391187d23a3344cf83ab48.jpg","机器学习","下列关于K值说法不正确的是()",[18,29,32,41,47,55,60],{"answer":19,"createTime":5,"id":20,"options":21,"question":26,"source":27,"type":28},[],102020569,[22,23,24,25],"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":30,"createTime":5,"id":6,"options":31,"question":16,"source":27,"type":28},[],[8,9,10,11],{"answer":33,"createTime":5,"id":34,"options":35,"question":40,"source":27,"type":28},[],102020572,[36,37,38,39],"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":42,"createTime":5,"id":43,"options":44,"question":45,"source":27,"type":46},[],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"( )"",2,{"answer":48,"createTime":5,"id":49,"options":50,"question":53,"source":27,"type":54},[],102020576,[51,52],"正确","错误","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",3,{"answer":56,"createTime":5,"id":57,"options":58,"question":59,"source":27,"type":54},[],102020578,[51,52],"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":27,"type":54},[],102020579,[51,52],"直观上看,我们希望"物以类聚",即聚类的结果"簇内相似度"高,且"簇间"相似度也高"]