[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fDgkbgEQIgJQrUFCYihVw0f4ZP1vOo0jzmC303KjIpog":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":19,"related":20,"source":30,"type":31},[],"2025-11-02 14:26:50",227706598,[8,9,10,11],"先验概率","样本相对于类标记的类条件概率(class-conditional probability)","类别的后验概率","证据",{"count":13,"courseId":14,"courseImg":15,"courseName":16,"workId":17,"workName":18},85,"53e1d2ef4961cca8eea3e23969ad2cb9","https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002F03a579384a6dc297c89809b582fcc767.png","默认课程","exam_167549860","机器学习初步","在推导朴素贝叶斯分类器公式时应用的贝叶斯定理中,似然指的是",[21,32,41,50,59,68,77,86,94,103],{"answer":22,"createTime":5,"id":23,"options":24,"question":29,"source":30,"type":31},[],227706589,[25,26,27,28],"泛化能力","建模能力","学习能力","拟合能力","模型在未见样本上表现好, 这一能力通常被称作模型的什么能力","v1",0,{"answer":33,"createTime":5,"id":34,"options":35,"question":40,"source":30,"type":31},[],227706590,[36,37,38,39],"划分后的信息熵-划分前的信息熵","划分前的信息熵\u002F划分后的信息熵","划分后的信息熵+划分前的信息熵","划分前的信息熵-划分后的信息熵","以下哪个选项是信息增益的定义",{"answer":42,"createTime":5,"id":43,"options":44,"question":49,"source":30,"type":31},[],227706591,[45,46,47,48],"错误率","查准率","查全率","精度","收购西瓜的公司希望把瓜摊的好瓜都尽量收走,请问他的评价标准是",{"answer":51,"createTime":5,"id":52,"options":53,"question":58,"source":30,"type":31},[],227706592,[54,55,56,57],"比较检验","评估方法","性能度量","以上三个选项都不需要考虑","为了说明模型在统计意义上表现好, 我们最需要考虑",{"answer":60,"createTime":5,"id":61,"options":62,"question":67,"source":30,"type":31},[],227706593,[63,64,65,66],"以很高概率得到很好的模型","以很低概率得到很好的模型","以很高概率得到不好的模型","以很低概率得到不好的模型","以下哪个是对概率近似正确(PAC)的正确解释",{"answer":69,"createTime":5,"id":70,"options":71,"question":76,"source":30,"type":31},[],227706594,[72,73,74,75],"属性值","类别标记","属性","样本","该课程视频中,训练数据中的&quot;色泽&quot;是什么",{"answer":78,"createTime":5,"id":79,"options":80,"question":85,"source":30,"type":31},[],227706595,[81,82,83,84],"21世纪以来,神经网络进入深度学习时代","1956年-1969年是神经网络研究冰河期,Minsky发表了Perceptron","1940年代是神经网络萌芽期,代表工作为感知机","1984年-1997年是繁荣期,代表工作为Hopfield和BP算法","对于不同时代对神经网络的研究以及代表工作说法正确的是()",{"answer":87,"createTime":5,"id":88,"options":89,"question":93,"source":30,"type":31},[],227706596,[90,91,92],"在正负类样本&quot;正中间&quot;的","靠近正类样本的","靠近负类样本的","对于线性可分的二分类任务样本集, 将训练样本分开的超平面有很多, 支持向量机试图寻找满足什么条件的超平面",{"answer":95,"createTime":5,"id":96,"options":97,"question":102,"source":30,"type":31},[],227706597,[98,99,100,101],"p(c_i)","p(c)","p(xc)","p(x|c)","贝叶斯公式中,估计后验概率 P(c|x) 的主要困难在于估计以下哪个选项",{"answer":104,"createTime":5,"id":6,"options":105,"question":19,"source":30,"type":31},[],[8,9,10,11]]