[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$fQQrkBbsZBHfrQ-TE7No6EKgF2h8gGrBgPiyiUs9WuzQ":3},{"answer":4,"createTime":5,"id":6,"options":7,"origin":12,"question":16,"related":17,"source":25,"type":36},[],"2025-08-29 14:57:20",1069778111,[8,9,10,11],"支持向量机只适用于线性分类问题","支持向量机通过最大化边界来找到最佳的分类超平面","支持向量机不适用于高维数据","支持向量机的目标是最小化训练样本的数量",{"courseId":13,"workId":14,"workName":15},"1000128448","61857010","第四章单元测试","支持向量机(SVM)是一种用于分类和回归分析的机器学习模型.下列关于支持向量机的说法中,哪一项是正确的",[18,27,37,48,51,60,69,78],{"answer":19,"createTime":5,"id":20,"options":21,"question":24,"source":25,"type":26},[],1069778077,[22,23],"正确","错误","在机器学习中,过拟合是指模型在训练数据上表现很好,但在测试数据上表现不佳的现象.根据这个定义,可以认为过拟合是模型学习到了训练数据中的噪声","v2",3,{"answer":28,"createTime":5,"id":29,"options":30,"question":35,"source":25,"type":36},[],1069778087,[31,32,33,34],"学习曲线可以清晰地显示训练集和验证集的准确率,以帮助判断模型是否过拟合","学习曲线仅展示训练集的准确率,无法反映模型的真实性能","学习曲线主要用于调整模型的超参数,直接影响模型的学习率","学习曲线不适用于评估复杂模型的性能,只能用于简单模型","在机器学习中,学习曲线是用来评估模型性能和判断过拟合的重要工具.以下哪项最能描述学习曲线在模型训练中的作用",0,{"answer":38,"createTime":5,"id":39,"options":40,"question":46,"source":25,"type":47},[],1069778094,[41,42,43,44,45],"支持向量机能够有效处理高维数据,适合特征维度大于样本数量的情况","支持向量机在处理非线性问题时,需要使用核函数进行转换,增加了模型的复杂性","支持向量机对噪声数据非常敏感,容易受到异常值的影响","支持向量机的计算复杂性较低,因此适合大规模数据集的处理","支持向量机可以通过选择合适的核函数来提高模型的泛化能力","支持向量机(SVM)在数据处理中的优势和缺点有哪些?以下哪些选项正确",1,{"answer":49,"createTime":5,"id":6,"options":50,"question":16,"source":25,"type":36},[],[8,9,10,11],{"answer":52,"createTime":5,"id":53,"options":54,"question":59,"source":25,"type":36},[],1069778143,[55,56,57,58],"欠拟合是指模型对训练数据的拟合程度过高,导致在新数据上表现不佳","过拟合是指模型在训练和测试数据上表现良好,但在应用数据上表现差","适当拟合是模型在训练和测试数据上均能保持良好的性能","过拟合是模型对训练数据拟合不足,未能捕捉到数据的特征","在机器学习中,模型的学习能力主要体现在拟合数据的能力上.以下关于欠拟合、过拟合和适当拟合的描述中,哪一项是正确的",{"answer":61,"createTime":5,"id":62,"options":63,"question":68,"source":25,"type":36},[],1069778149,[64,65,66,67],"模型过于复杂,参数过多,导致对训练数据的噪声进行学习","模型的复杂度适中,能够良好地捕捉数据的基本趋势","使用了足够的训练数据,避免了训练数据的偏差","模型的正则化技术得当,能够有效减少过拟合","在机器学习中,过拟合指的是模型在训练数据上表现良好,但在新的未见数据上表现不佳的现象.这种现象通常是由于什么原因导致的",{"answer":70,"createTime":5,"id":71,"options":72,"question":77,"source":25,"type":47},[],1069778212,[73,74,75,76],"决策树是一种通过树形结构进行分类和回归的模型","决策树的每个节点代表一个特征的测试","决策树的深度越大,模型的复杂度越低","决策树可以处理分类和数值型数据","以下关于"决策树"的描述中,哪些是正确的",{"answer":79,"createTime":5,"id":80,"options":81,"question":84,"source":25,"type":26},[],1069778264,[82,83],"对","错","信息熵是用来量化系统中不确定性的一种度量,越高的信息熵表示系统的不确定性越小,越低的信息熵表示系统的不确定性越大.以上描述是正确的吗"]