[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fQKdLzccqd_xW1IVAOPba3t7XGfjUsIfXrTuru9kP6nU":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":10,"question":13,"related":14,"source":24,"type":76},[],"2025-12-26 09:52:28",1076141152,[8,9],"对","错",{"courseImg":11,"courseName":12},"https:\u002F\u002Ftihai-oss-cloud.itihey.com\u002Fimg\u002Fcd518c690d5b9dbacf33f77752e1df8d.jpg","机器学习","k均值算法可看作是高斯混合聚类在混合成分方差相等、且每个样本仅指派给一个混合成分时的特例.( )",[15,26,36,45,54,63,71,77,80,89],{"answer":16,"createTime":5,"id":17,"options":18,"question":23,"source":24,"type":25},[],1076140877,[19,20,21,22],"K-Means","决策树","支持向量机","kNN","选项中哪些方法不可以直接来对文本分类?( )","v2",0,{"answer":27,"createTime":5,"id":28,"options":29,"question":34,"source":24,"type":35},[],1076140886,[30,31,32,33],"对训练集随机采样,在随机采样的数据上建立模型","尝试使用在线机器学习算法","使用 PCA 算法减少特征维度","选项中没有正确答案","我们想要训练一个 ML 模型,样本数量有 100 万个,特征维度是 5000,面对如此大数据,如何有效地训练模型?( )",1,{"answer":37,"createTime":5,"id":38,"options":39,"question":44,"source":24,"type":25},[],1076140898,[40,41,42,43],"Ridge 回归适用于特征选择","Lasso 回归适用于特征选择","两个都适用于特征选择","选项中的说法都不对","关于特征选择,下列对 Ridge 回归和 Lasso 回归说法正确的是?( )",{"answer":46,"createTime":5,"id":47,"options":48,"question":53,"source":24,"type":25},[],1076140911,[49,50,51,52],"若 λ 较大时,偏差减小,方差减小","若 λ 较大时,偏差减小,方差增大","若 λ 较大时,偏差增大,方差减小","若 λ 较大时,偏差增大,方差增大","假如使用一个较复杂的回归模型来拟合样本数据,使用 Ridge 回归,调试正则化参数 λ,来降低模型复杂度.若 λ 较大时,关于偏差(bias)和方差(variance),下列说法正确的是?( )",{"answer":55,"createTime":5,"id":56,"options":57,"question":62,"source":24,"type":35},[],1076141013,[58,59,60,61],"纯度高的结点需要更多的信息来描述它","信息增益可以用"1比特-熵"获得","如果选择一个属性具有许多特征值, 那么这个信息增益是有偏差的","选项中说法都不对","在决策树分割结点的时候,下列关于信息增益说法正确的是( )",{"answer":64,"createTime":5,"id":65,"options":66,"question":70,"source":24,"type":35},[],1076141099,[67,68,69,61],"信息增益准则对可取值数目较多的属性有所偏好","增益率准则对可取值数目较少的属性有所偏好","C4.5算法并不是直接选择增益率最大的候选划分属性,而是先从候选划分属性中找出信息增益高于平均水平的属性,再从中选择增益率最高的","对于划分属性选择,选项中说法正确的是( )",{"answer":72,"createTime":5,"id":73,"options":74,"question":75,"source":24,"type":76},[],1076141119,[8,9],"在决策树学习过程中,如果当前结点划分属性为连续属性,那么该属性还可作为其后代结点的划分属性.( )",3,{"answer":78,"createTime":5,"id":6,"options":79,"question":13,"source":24,"type":76},[],[8,9],{"answer":81,"createTime":5,"id":82,"options":83,"question":88,"source":24,"type":25},[],1076141221,[84,85,86,87],"C = 0","C = 1","C 正无穷大","C 负无穷大","假设我们使用原始的非线性可分版本的 Soft-SVM 优化目标函数.我们需要做什么来保证得到的模型是线性可分离的?( )",{"answer":90,"createTime":5,"id":91,"options":92,"question":96,"source":24,"type":25},[],1076141368,[93,94,95],"监督学习","非监督学习","半监督学习","智能化中医望诊时,对一幅舌脉图像(伸出舌头的人脸图像),希望把舌头部分从人脸的其他部分划分出来,可以采用以下方法:将整幅图的每个象素的属性记录在一张数据表中,然后用某种方法将这些数据按它们的自然分布状况划分成两类.因此每个象素就分别得到相应的类别号,从而实现了舌头图像的分割.那么这种方法属于:( )"]