zoukankan      html  css  js  c++  java
  • Random Forest

    Motivation

    Random Forest is some kind of ensemble learning method, just as its name indicates. The base learner is decision tree and RF uses Bagging to integrate. The difference mainly lies in the word 'Random'. The original decision tree selects the best attribute while RF has two steps to select a split attribute for each base learner:

    • Select k attributes from (A) randomly;
    • Select the best attribute from the k attributes. If k=1 then it's total random selection; If k=(|A|) then it's the same as decision tree. The recommended is (k=log_2|A|).

    The intuition behind this is to increase the diversity of the base learners. In original bagging method we just use the sample disturbance of the training data. But in RF we add the attribute disturbance thus making the learner generalize very well.

    As you can imagine, RF's performance is worse than Bagging during the early time of the training process. Since we just use a subset of the attributes so the base learner performs not that well. But with the increase of the number of base learners, it will gradually use the whole information and then has a low generation error. By the way, RF is often faster than Bagging since we only use a subset to train the base learners.

    Details

    Example

    Implementation

    Properties

  • 相关阅读:
    冲刺成果演示
    c#输入串字符不符
    课堂测试—数据清洗
    mapreduce实验
    问题纠错
    软件需求阅读笔记第一篇
    暑假第五周总结
    暑假第四周总结
    暑假第三周总结
    暑假第二周总结
  • 原文地址:https://www.cnblogs.com/EIMadrigal/p/11801828.html
Copyright © 2011-2022 走看看