代码空间


摘要(Abstract)

特征工程很好的混合了专业领域知识、直觉和基本的数学能力。 时间戳处理 分解类别属性 分箱/分区 交叉特征 特征选择 特征缩放 特征提取 异常数据的清洗和样本的选取 数据预处理 无量纲化 标准化 区间缩放法 归一化 对定量特征二值化(离散化) 对定性特征进行独热编码 缺失值的处理 删除 统计填充 统一填充 预测填充 具体分析 数据变换 特征选择 过滤法,包装法,嵌入法 Filter方差选择法, 相关系数法,卡方检验,互信息法,Wrapper,递归特征消除法,Embedded,基于惩罚项的特征选择法,基于树模型的特征选择法,训练能够对特征打分的预选模型:GBDT、RandomForest和Logistic Regression等都能对模型的特征打分,通过打分获得相关性后再训练最终模型; 特征组合,降维,主成分分析法(PCA),线性判别分析法(LDA)


主题(Topic)

robust-learning reinforcement-learning reinforcement-learning- robust-machine-learning adversarial-reinforcement-learning applied-reinforcement-learning berkeley-reinforcement-learning carla-reinforcement-learning causal-reinforcement-learning checkers-reinforcement-learning constrained-reinforcement-learning continuous-reinforcement-learning coursera-reinforcement-learning curriculum-reinforcement-learning deep-reinforcement-learning dissecting-reinforcement-learning distributed-reinforcement-learning feudal-reinforcement-learning hierachical-reinforcement-learning hierarchical-reinforcement-learning interactive-reinforcement-learning inverse-reinforcement-learning meta-reinforcement-learning minecraft-reinforcement-learning multiagent-reinforcement-learning munchausen-reinforcement-learning practical-reinforcement-learning pytorch-reinforcement-learning recurrent-reinforcement-learning reinforcement-learning-agent reinforcement-learning-agents reinforcement-learning-alberta reinforcement-learning-algorithm reinforcement-learning-algorithms reinforcement-learning-analysis reinforcement-learning-books reinforcement-learning-bot reinforcement-learning-bots reinforcement-learning-cartpole reinforcement-learning-concepts reinforcement-learning-datasets reinforcement-learning-environ reinforcement-learning-environment reinforcement-learning-environments reinforcement-learning-examples reinforcement-learning-excercises reinforcement-learning-exercises reinforcement-learning-frame reinforcement-learning-options
项目(Project)