【第四课】kaggle案例分析四

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比赛题目介绍

• facebook想要准确的知道用户登录的地点，从而可以为用户提供更准确的服务
• 为了比赛，facebook创建了一个虚拟世界地图，地图面积为100km2，其中包含了超过1000000个地点
• 通过给定的坐标，以及坐标准确性，判断用户登录地点
• 训练集和测试集是根据时间划分的，而在公共排行榜和私人排行榜上的测试集数据是随机划分的
• row_id 登录事件的id，作为标识符使用
• x，y：坐标数值
• accuracy：坐标的准确性
• time：时间戳
• place_id：地点id，需要预测的变量
• 其中，accuracy和time的具体含义并没有给出，关于这两个变量的探索也是比赛的一部分内容

XGboost

• XGboost就是梯度提升树的改进(速度快)

• kaggle神器 XGboost

• 模型： 如何在已知xi​而预测y^​i​

• 线性模型：y^​i​=∑j​wj​xij​包含线性模型和逻辑回归模型

• 预测分数y^​i​可以有基于任务的不同解读

  • 线性回归 y^​i​是预测分数
  • 逻辑回归 1+exp(−y^​i​)1​是对积极的实例的可能性预测
  • 其他，比如排名预测

• 参数：我们需要从数据中学习到的参数

• 线性模型：wj​∣j=1,...,d

• 损失函数的使用

• Obj(Θ)=L(Θ)+Ω(Θ)

• 训练数据中的损失：L=∑i=1n​l(yi​,y^​i​)

  • 方差损失 l(yi​,y^​i​)=(yi​−y^​i​)2
  • 逻辑损失 l(yi​,y^​i​)=yi​ln(1+e−y^​i​)+(1−yi​)ln(1+eey^​i​)

• 模型的复杂度

  • L2规范 Ω(w)=λ∣∣w∣∣2
  • L1规范 Ω(w)=λ∣∣w∣∣1​

• 正则项(惩罚模型的复杂度) ∑i=1n​(yi​−wTxi​)2+λ∣∣w∣∣2

• Lasso ∑i=1n​(yi​−wTxi​)2+λ∣∣w∣∣1​

• 逻辑回归 ∑i=1n​[yi​ln(1+e−wTxi​)+(1−yi​)ln(1+ewTxi​)]+λ∣∣w∣∣2

回归树

• 线性回归问题就是用折线或者折平面(高维度)去拟合训练集
• 用小的决策树，不剪枝，用投票的方式将决策树组合起来
• 折线回归树预测：

• y^​i​=k=1∑K​fk​(xi​),fk​∈F

数据探索

特征工程

• 与坐标相关的特征
• 与时间相关的特征
• 与准确性相关的特征
• Z-值

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转载于:https://www.cnblogs.com/pandaboy1123/p/10405354.html