Coder Social home page Coder Social logo

chengstone / kaggle_criteo_ctr_challenge- Goto Github PK

View Code? Open in Web Editor NEW
358.0 15.0 128.0 2.31 MB

This is a kaggle challenge project called Display Advertising Challenge by CriteoLabs at 2014.这是2014年由CriteoLabs在kaggle上发起的广告点击率预估挑战项目。

License: MIT License

Jupyter Notebook 88.94% Shell 0.01% Makefile 0.06% C++ 10.55% Perl 0.17% C 0.26%

kaggle_criteo_ctr_challenge-'s Introduction

kaggle_criteo_ctr_challenge-

This is a kaggle challenge project called Display Advertising Challenge by CriteoLabs at 2014. 这是2014年由CriteoLabs在kaggle上发起的广告点击率预估挑战项目。 使用TensorFlow1.0和Python 3.5开发。

Author chengstone

e-Mail [email protected]

代码详解请参见jupyter notebook和↓↓↓

知乎专栏:https://zhuanlan.zhihu.com/p/32500652

博客:http://blog.csdn.net/chengcheng1394/article/details/78940565

欢迎转发扩散 ^_^

本文使用GBDT、FM、FFM和神经网络构建了点击率预估模型。

网络模型

image

LogLoss曲线

image

验证集上的训练信息

  • 平均准确率
  • 平均损失
  • 平均Auc
  • 预测的平均点击率
  • 精确率、召回率、F1 Score等信息

image

更多内容请参考代码,Enjoy!

许可

Licensed under the MIT License with the 996ICU License.

kaggle_criteo_ctr_challenge-'s People

Contributors

chengstone avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

kaggle_criteo_ctr_challenge-'s Issues

gbdt的叶子节点作为fm的输入

有个问题请教下,
文章关于使用GBDT的叶子节点作为FM的输入:
GBDT已经训练好了,我们需要GBDT输出的叶子节点作为输入数据X传给FM,一共30个叶子节点,那么输入给FM的数据格式就是X中不是0的数据的index:value。

但是我看代码里面好像是把gbdt的每棵树的输出作为FM的输入的。

因为lightgbm中的树个数是32,叶子节点是30,如果是叶子节点的输出作为FM的输入的话,那FM的输入维度应该是30,而不是32。不知道是不是我理解的有问题。

另外,在计算FM的输入时代码如下:

for i in range(tree_counts):
train_leaves[:, i] = train_leaves[:, i] + tree_info[i]['num_leaves'] * i + 1
valid_leaves[:, i] = valid_leaves[:, i] + tree_info[i]['num_leaves'] * i + 1
test_leaves[:, i] = test_leaves[:, i] + tree_info[i]['num_leaves'] * i + 1

这段代码该如何理解呢,为什么每棵树的输出都要加上“tree_info[i]['num_leaves'] * i + 1” 呢

多谢

输出的文件名为*.out.logit

在FFM 那里, 输出的文件名为*.out.logit

但是命令里面并没有指定是out.logit,我在libFFM的文档里面也没有找到相关的信息,请问是哪里指定了输出文件名是.out.logit 呢 另外*.out.logit文件里面记录的是什么呢,和*.out什么区别呢

谢谢!

调用libffm出错

colden@server602:~/RS/kaggle_criteo_ctr_challenge-$ ./libffm/libffm/ffm-train --auto-stop -r 0.1 -t 32 -s 1 -p ./data/valid_ffm.txt ./data/train_ffm.txt model_ffm
-bash: ./libffm/libffm/ffm-train: cannot execute binary file: Exec format error

不知道为什么

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.