On the Bias/Variance tradeoff in Machine Learning

参考:https://codesachin.wordpress.com/2015/08/05/on-the-biasvariance-tradeoff-in-machine-learning/

之前一直没搞明白什么是bias,什么是variance,现在看看这篇博文。

当你的模型太简单,也就是你的train error太大的时候,你的bias就会比较大;当你的模型变得复杂时,bias变小,同时模型变得比较senstive,variance就会变大

但bias变化的幅度更大,所有整体看来,cross-validation仍然是下降的。当过了某个点后,情况逆转。

时间: 2024-12-12 08:06:00

On the Bias/Variance tradeoff in Machine Learning的相关文章

2.9 Model Selection and the Bias–Variance Tradeoff

结论 模型复杂度↑Bias↓Variance↓ 例子 $y_i=f(x_i)+\epsilon_i,E(\epsilon_i)=0,Var(\epsilon_i)=\sigma^2$ 使用knn做预测,在点$x_0$处的Excepted prediction error: $EPE(x_0)=E\left[\left(y_0-\hat{f}(x_0)\right)^2|x_0\right]\\ \ \ =E\left[\left(y_0-E(y_0)\right)^2|x_0\right]+\l

machine learning学习笔记

看到Max Welling教授主页上有不少学习notes,收藏一下吧,其最近出版了一本书呢还,还没看过. http://www.ics.uci.edu/~welling/classnotes/classnotes.html Statistical Estimation [ps]- bayesian estimation- maximum a posteriori (MAP) estimation- maximum likelihood (ML) estimation- Bias/Variance

机器学习(Machine Learning)&深度学习(Deep Learning)资料

机器学习(Machine Learning)&深度学习(Deep Learning)资料 <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最新版本

机器学习(Machine Learning)&amp;深入学习(Deep Learning)资料

<Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost 到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室 Jurgen Schmidhuber 写的最新版本<神经网络与深度学习综述>本综述的特点是以时间排序,从 1940 年开始讲起,到

机器学习(Machine Learning)&amp;amp;深度学习(Deep Learning)资料

机器学习(Machine Learning)&深度学习(Deep Learning)资料 機器學習.深度學習方面不錯的資料,轉載. 原作:https://github.com/ty4z2008/Qix/blob/master/dl.md 原作作者會不斷更新.本文更新至2014-12-21 <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍非常全面.从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep L

CheeseZH: Stanford University: Machine Learning Ex5:Regularized Linear Regression and Bias v.s. Variance

源码:https://github.com/cheesezhe/Coursera-Machine-Learning-Exercise/tree/master/ex5 Introduction: In this exercise, you will implement regularized linear regression and use it to study models with different bias-variance properties. 1. Regularized Lin

[C5] Andrew Ng - Structuring Machine Learning Projects

About this Course You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been

Introduction to Machine Learning

Chapter 1 Introduction 1.1 What Is Machine Learning? To solve a problem on a computer, we need an algorithm. An algorithm is a sequence of instructions that should be carried out to transform the input to output. For example, one can devise an algori

【转载】COMMON PITFALLS IN MACHINE LEARNING

COMMON PITFALLS IN MACHINE LEARNING JANUARY 6, 2015 DN 3 COMMENTS Over the past few years I have worked on numerous different machine learning problems. Along the way I have fallen foul of many sometimes subtle and sometimes not so subtle pitfalls wh