77 / 852 复盘和代码改进
License: MIT License
比赛任务为表格数据的回归问题。特点为train data较大，达到了GB级别。这就导致了在特征工程阶段使用pandas处理速度不够。同时特征维度较高，可进行的特征构造较多。
采取了分批次处理的方法，通常全量数据需要分成20-25个epoch才能完全处理。在进行代码复盘时，参考了高分的参考方案，即针对不同的数据，通过不同的Class来定义数据结构，脱离了pandas的框架进行特征处理，构造的特征以字典的形式返回，加快了特征的处理速度。
进行了多次的特征构造，同时结合了lag特征，滑窗特征和针对不同player和team的统计特征对数据的信息进行抽取。
是否可以结合MLB的专业知识，针对不同的player和team，基于他们的统计特征，先行构造embedding特征，抽取出player和team对应的embedding向量？ 是否可以结合spark框架，改进出pyspark版本的code，缩短代码运行的时间？
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