[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f5kRMgRCMH0_xsbmyoTd9ll50PYkyuMi00Lr017Q-A9k":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":18,"related":19,"source":29,"type":30},[],"2025-12-11 22:10:23",1073685209,[8,9,10,11],"让距离分类线最近的样本距离分类线最远","使用sigmoid函数判定样本的概率","使用二叉树原理进行分类","使用距离判定,如果距离某个类别样本越近,就属于哪一个类别",{"courseId":13,"courseImg":14,"courseName":15,"workId":16,"workName":17},"1000086781","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F728fb4077cb5a2192c5eced886ea5290.png","[共享课]信息技术-拓展模块","62260157","第六章单元测试","SVM的分类原理为( )",[20,31,34,43,52,61,69,78],{"answer":21,"createTime":5,"id":22,"options":23,"question":28,"source":29,"type":30},[],1073685206,[24,25,26,27],"0-1","0-9","0-255","0-100","灰度的取值范围是( )","v2",0,{"answer":32,"createTime":5,"id":6,"options":33,"question":18,"source":29,"type":30},[],[8,9,10,11],{"answer":35,"createTime":5,"id":36,"options":37,"question":42,"source":29,"type":30},[],1073685219,[38,39,40,41],"K值的选择会对KNN模型的结果产生重大影响","当选择较大的K值时,会导致预测效果变差","当选择较小的K值时,模型变得敏感,受噪声点影响较小","在实际应用中,K一般取比较小的数值","KNN中K的取值说法错误的是( )",{"answer":44,"createTime":5,"id":45,"options":46,"question":51,"source":29,"type":30},[],1073685222,[47,48,49,50],"投票策略","距离策略","概率策略","数量策略","KNN决策方法为( )",{"answer":53,"createTime":5,"id":54,"options":55,"question":60,"source":29,"type":30},[],1073685225,[56,57,58,59],"辅助调参,查找模型最优参数","加快模型运行速度","提高模型准确率","使用多种模型测评得分","网格搜索交叉验证的作用为( )",{"answer":62,"createTime":5,"id":63,"options":64,"question":68,"source":29,"type":30},[],1073685231,[65,57,66,67],"避免噪声影响","简化图像颜色差别","缩小图像类别差异性","对数据进行归一化处理的好处是( )",{"answer":70,"createTime":5,"id":71,"options":72,"question":77,"source":29,"type":30},[],1073685236,[73,74,75,76],"最终的类别数量","一般取较大的数","距离未知样本最近的邻居数量","距离未知样本最近的K个概率","KNN算法中的K代表的是( )",{"answer":79,"createTime":5,"id":80,"options":81,"question":86,"source":29,"type":30},[],1073685244,[82,83,84,85],"1","2","3","4","KNN算法默认的距离计算方式p的值为( )"]