[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fBVdnwJ2OkwdDaRZ0j-GkgSO-LrY43I5a4eXeI511wJw":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":19,"related":20,"source":30,"type":31},[],"2026-01-10 17:33:24",258964115,[8,9,10,11],"0-1损失","铰链损失","交叉熵损失","均方误差(MSE)",{"count":13,"courseId":14,"courseImg":15,"courseName":16,"workId":17,"workName":18},70,"53e1d2ef4961cca8eea3e23969ad2cb9","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F03a579384a6dc297c89809b582fcc767.png","默认课程","work_48352413","第六讲作业","以下哪项属于回归问题中常见的损失函数",[21,32,41,44,53,62,69,78,87,96],{"answer":22,"createTime":5,"id":23,"options":24,"question":29,"source":30,"type":31},[],258964113,[25,26,27,28],"线性回归","监督学习算法","无监督学习算法","K-means聚类","手写数字识别问题通常采用哪种类型的机器学习算法","v1",0,{"answer":33,"createTime":5,"id":34,"options":35,"question":40,"source":30,"type":31},[],258964114,[36,37,38,39],"减少特征的维度","更换模型的类型","让模型更好地拟合数据,降低损失","增加数据的样本数量","在机器学习中,"迭代训练"的目的是",{"answer":42,"createTime":5,"id":6,"options":43,"question":19,"source":30,"type":31},[],[8,9,10,11],{"answer":45,"createTime":5,"id":46,"options":47,"question":52,"source":30,"type":31},[],258964116,[48,49,50,51],"聚类中心","系数","斜率","截距","线性回归模型的参数不包括",{"answer":54,"createTime":5,"id":55,"options":56,"question":61,"source":30,"type":31},[],258964117,[57,58,59,60],"年龄","身高","体重","性别","根据身高预测体重的问题中,自变量是",{"answer":63,"createTime":5,"id":64,"options":65,"question":68,"source":30,"type":31},[],258964118,[66,25,28,67],"决策树分类","支持向量机","以下哪项不是监督学习算法",{"answer":70,"createTime":5,"id":71,"options":72,"question":77,"source":30,"type":31},[],258964119,[73,74,75,76],"回归问题","降维问题","聚类问题","分类问题","逻辑回归虽然名字中有"回归",但实际上常用于解决",{"answer":79,"createTime":5,"id":80,"options":81,"question":86,"source":30,"type":31},[],258964120,[82,83,84,85],"预先设定的规则","带标签的训练数据","与环境的交互和奖励信号","无标签的数据集","强化学习中,智能体通过什么来学习最优行为策略",{"answer":88,"createTime":5,"id":89,"options":90,"question":95,"source":30,"type":31},[],258964121,[91,92,93,94],"处理数据并学习规律","升级硬件设备","人工手动调整参数","分析历史代码","机器学习中,模型通过什么来不断优化自身性能",{"answer":97,"createTime":5,"id":98,"options":99,"question":104,"source":30,"type":31},[],258964122,[100,101,102,103],"增加数据的维度","告诉模型样本的正确输出","标识数据的存储位置","区分不同的特征","在监督学习中,标签的作用是"]