[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fpqdHbchDZxokTE-1n8219zTjAJqN_rXPjzKJf5jwxuU":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":18,"related":19,"source":29,"type":30},[],"2025-04-11 08:33:56",182605481,[8,9,10,11],"训练集;需要","训练集;不需要","验证集;需要","验证集;不需要",{"count":13,"courseId":14,"courseImg":15,"courseName":16,"workId":17,"workName":16},85,"fc0c1d00e84a5bdd920ca3eef531981e","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002Ffb47e65ee6bbb8f896e6b4fca1783b07.png","机器学习初步","exam_151611981","我们通常将数据集划分为训练集,验证集和测试集进行模型的训练,参数的验证需要在()上进行,参数确定后()重新训练模型",[20,31,40,49,52,61,70,79,88,97],{"answer":21,"createTime":5,"id":22,"options":23,"question":28,"source":29,"type":30},[],182605478,[24,25,26,27],"自顶向下","自底向上","随机顺序","以上答案都不对","后剪枝过程在生成完全决策树后,()对树中所有非叶结点进行考察","v1",0,{"answer":32,"createTime":5,"id":33,"options":34,"question":39,"source":29,"type":30},[],182605479,[35,36,37,38],"分而治之","集成","聚类","排序","决策树学习的策略是什么",{"answer":41,"createTime":5,"id":42,"options":43,"question":48,"source":29,"type":30},[],182605480,[44,45,46,47],"CART算法在候选属性集合中选取使划分后基尼指数最大的属性","划分选择的各种准则对泛化性能的影响有限","划分选择的各种准则对决策树尺寸有较大影响","相比划分准则,剪枝方法和程度对决策树泛化性能的影响更为显著","下列说法错误的是()",{"answer":50,"createTime":5,"id":6,"options":51,"question":18,"source":29,"type":30},[],[8,9,10,11],{"answer":53,"createTime":5,"id":54,"options":55,"question":60,"source":29,"type":30},[],182605482,[56,57,58,59],"经验","专家","规则","实践","机器学习的经典定义是:利用 () 改善系统自身的性能",{"answer":62,"createTime":5,"id":63,"options":64,"question":69,"source":29,"type":30},[],182605483,[65,66,67,68],"所有&quot;问题&quot;难度不同","所有&quot;问题&quot;出现的机会服从任意分布","所有&quot;问题&quot;出现的机会不相同","所有&quot;问题&quot;出现的机会相同","以下哪个选项是NFL定理的重要前提",{"answer":71,"createTime":5,"id":72,"options":73,"question":78,"source":29,"type":30},[],182605484,[74,75,76,77],"无序的离散属性","连续属性","有序的离散属性","以上都对","示例的属性可以属于下列哪个类别",{"answer":80,"createTime":5,"id":81,"options":82,"question":87,"source":29,"type":30},[],182605485,[83,84,85,86],"算法","数据","任务需求","以上选项都是","&quot;好&quot;模型取决于下列哪些因素",{"answer":89,"createTime":5,"id":90,"options":91,"question":96,"source":29,"type":30},[],182605486,[92,93,94,95],"较高的簇内相似度","较低的簇内相似度","较大的簇间距离","和某个参考模型的结果相似","一个好的聚类可能存在的特征通常不包括",{"answer":98,"createTime":5,"id":99,"options":100,"question":105,"source":29,"type":30},[],182605487,[101,102,103,104],"对每个样本 x 选择能使后验概率 P(c∣x) 最大的类别标记","对每个样本 x 选择能使后验概率 P(c∣x) 最小的类别标记","对每个样本 x 选择能使条件风险 R(c_i∣x) 最大的类别标记","对每个样本 x 选择能使条件风险 R(c_i∣x)最小的类别标记","以下哪个选项是对贝叶斯最优分类器的描述"]