文档章节

What are hyperparameters in machine learning?

日拱一卒
 日拱一卒
发布于 2016/08/20 09:44
字数 281
阅读 39
收藏 0

In machine learning, we use the term hyperparameter to distinguish from standard model parameters. So, it is worth to first understand what those are.

A machine learning model is the definition of a mathematical formula with a number of parameters that need to be learned from the data. That is the crux of machine learning: fitting a model to the data. This is done through a process known as model training. In other words, by training a model with existing data, we are able to fit the model parameters.

However, there is another kind of parameters that cannot be directly learned from the regular training process. These parameters express “higher-level” properties of the model such as its complexity or how fast it should learn. They are called hyperparameters. Hyperparameters are usually fixed before the actual training process begins.

So, how are hyperparameters decided? That is probably beyond the scope of this question, but suffice to say that, broadly speaking, this is done by setting different values for those hyperparameters, training different models, and deciding which ones work best by testing them.

So, to summarize. Hyperparameters:

  • Define higher level concepts about the model such as complexity, or capacity to learn.
  • Cannot be learned directly from the data in the standard model training process and need to be predefined.
  • Can be decided by setting different values, training different models, and choosing the values that test better

Some examples of hyperparameters:

  • Number of leaves or depth of a tree
  • Number of latent factors in a matrix factorization
  • Learning rate (in many models)
  • Number of hidden layers in a deep neural network
  • Number of clusters in a k-means clustering

本文转载自:https://www.quora.com/Machine-Learning/What-are-hyperparameters-in-machine-learning

上一篇: 统计学习理论
下一篇: Python命名规范
日拱一卒
粉丝 16
博文 63
码字总数 24558
作品 0
沈阳
其他
私信 提问
分布式机器学习框架-TensorFlow on Spark(英)

TensorFlow还可以在Spark上分布式地运行哦! 尽管TensorFlow也开放了自己的分布式运行框架,但在Spark上可以更容易地使用已有的知识和软硬件基础设施,而且更加开放。 The integration of T...

openthings
2016/03/09
3K
2
机器学习入门 - 1. 介绍与决策树(decision tree)

机器学习(Machine Learning) 介绍与决策树(Decision Tree) 机器学习入门系列 是 个人学习过程中的一些记录与心得。其主要以要点形式呈现,简洁明了。 1.什么是机器学习? 一个比较概括的理解...

SummerTime2017
2018/06/28
0
0
Improving Deep Neural Networks学习笔记(三)

文章作者:Tyan 博客:noahsnail.com | CSDN | 简书 5. Hyperparameter tuning 5.1 Tuning process Hyperparameters: , , , layers, hidden units, learning rate decay, mini-batch size. T......

Quincuntial
2017/09/23
0
0
Course2-week3-hyperparameterTuning - BatchNormalization - Framework

版权声明:本文为博主原创文章,未经博主允许不得转载。 https://blog.csdn.net/robinXushuai/article/details/80625250 hyperparameter tuning 1 - tuning process How to systematically o......

_席达_
2018/06/08
0
0
人工智能资料库:第72辑(20171203)

1.【会议】Bayesian Deep Learning 简介: While deep learning has been revolutionary for machine learning, most modern deep learning models cannot represent their uncertainty nor......

chen_h
2017/12/03
0
0

没有更多内容

加载失败,请刷新页面

加载更多

代理模式之JDK动态代理 — “JDK Dynamic Proxy“

动态代理的原理是什么? 所谓的动态代理,他是一个代理机制,代理机制可以看作是对调用目标的一个包装,这样我们对目标代码的调用不是直接发生的,而是通过代理完成,通过代理可以有效的让调...

code-ortaerc
今天
4
0
学习记录(day05-标签操作、属性绑定、语句控制、数据绑定、事件绑定、案例用户登录)

[TOC] 1.1.1标签操作v-text&v-html v-text:会把data中绑定的数据值原样输出。 v-html:会把data中值输出,且会自动解析html代码 <!--可以将指定的内容显示到标签体中--><标签 v-text=""></......

庭前云落
今天
7
0
VMware vSphere的两种RDM磁盘

在VMware vSphere vCenter中创建虚拟机时,可以添加一种叫RDM的磁盘。 RDM - Raw Device Mapping,原始设备映射,那么,RDM磁盘是不是就可以称作为“原始设备映射磁盘”呢?这也是一种可以热...

大别阿郎
今天
10
0
【AngularJS学习笔记】02 小杂烩及学习总结

本文转载于:专业的前端网站☞【AngularJS学习笔记】02 小杂烩及学习总结 表格示例 <div ng-app="myApp" ng-controller="customersCtrl"> <table> <tr ng-repeat="x in names | orderBy ......

前端老手
昨天
14
0
Linux 内核的五大创新

在科技行业,创新这个词几乎和革命一样到处泛滥,所以很难将那些夸张的东西与真正令人振奋的东西区分开来。Linux内核被称为创新,但它又被称为现代计算中最大的奇迹,一个微观世界中的庞然大...

阮鹏
昨天
18
0

没有更多内容

加载失败,请刷新页面

加载更多

返回顶部
顶部