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gbdt-lr's Introduction

gbdt+lr

参考Facebook论文：Practical Lessons from Predicting Clicks on Ads at Facebook

原理：GBDT是一种常用的非线性模型，基于集成学习中boosting的**，由于GBDT本身可以发现多种有区分性的特征以及特征组合，决策树的路径可以直接作为LR输入特征使用，省去了人工寻找特征、特征组合的步骤。所以可以将GBDT的叶子结点输出，作为LR的输入，如图所示：

数据集

数据集采用criteo-Display Advertising Challenge比赛的部分数据集，比赛地址： https://www.kaggle.com/c/criteo-display-ad-challenge/data 全部的数据集下载：http://labs.criteo.com/2014/02/kaggle-display-advertising-challenge-dataset/

lr:

train-logloss: 0.117204349458
val-logloss: 0.594054488135

gbdt:

train-logloss: 0.319873
val-logloss: 0.521285

gbdt+lr:

tr-logloss: 0.261914566568
val-logloss: 0.502417918338

综上：gbdt+lr的val-logloss最小。但是gbdt+lr并不是适用于所有的业务数据，当存在高度稀疏特征的时候，线性模型一般会优于非线性模型。

FFM：

gbdt+FFM：

gbdt+FFM的效果要优于FFM

FFM见https://github.com/wangru8080/FFMFormat
其中FFM使用的是libffm库来训练，代码仅给出了构造数据输入的方法（FFMFormat），构造好输入格式后，直接使用libFFM训练即可

为了快速验证实验，实验中所使用的数据集非常小，所以gbdt+FFM的效果在这个数据集下未必会好于gbdt+lr，至于gbdt+FFM与gbdt+lr的效果哪个更好，在以后的学习过程中还需要进行多次试验

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