Multiclass classification例子: 邮箱的邮件的分类: 工作邮件,私人邮件,朋友的邮件,兴趣爱好的邮件 医学诊断: 没有生病,患有流感,患有普通感冒 天气: 晴天,兩,多云等 One-vs-all classfication = one-vs-rest : 每一次将一个class分出来,共构建3个classifiers hθ(i)(x) = P(y=i|x;θ)    (i=1;2;3) train a logistic regression classifier hθ(i…

machine learning(9) -- classification:Decision boundary 上图的decision boundary是一条直线,是属于预测函数的一个属性(当参数已经确定),不是数据集的属性. decision boundary是根据预测函数得来的,而不是根据数据集得到的.数据集(trainning data)是用来确定参数(θ0,θ1,θ2....)的 上图的decision boundary不是直线而是一个圆,需要create more features f…

其它的比gradient descent快, 在某些场合得到广泛应用的求cost function的最小值的方法 when have a large machine learning problem,一般会使用这些advanced optimization algorithm而不是gradient descent Conjugate gradient, BFGS,L-BFGS很复杂,可以在不明白详细原理的情况下进行应用(使用software libary). 可以使用Octave和matlab的…

分类预测不能使用linear regression, linear regression算法对于分类预测效果很差,应使用logistic regression算法 Logistic regresstion = a Classification algorithm 一种分类预测算法 Logistic regression model:  Sigmoid function = Logistic function   Logistic function的曲线图(y值处于0与1之间[z->+∞时g(z)…

logistic regression cost function(single example) 图像分布 logistic regression cost function(m examples) Writting cost function in a more convenient form with just one line To fit parameter θ Using gradient descent to minimize cost function 看上去和gradient…

How do you explain Machine Learning and Data Mining to non Computer Science people?   Pararth Shah, ML Enthusiast Answered Dec 22, 2012 · Featured on VentureBeat · Upvoted by Melissa Dalis, CS & Math major at Duke and Alberto Bietti, PhD student in m…

[1] ML Introduction a. supervised learning & unsupervised learning 监督学习:从给定的训练数据集中学习出一个函数(模型参数),当新的数据到来时,可以根据这个函数预测结果.监督学习的训练集要求包括输入输出,也可以说是特征和目标.训练集中的目标是由人标注的.常用于:训练神经网络.决策树.回归分析.统计分类 无监督学习:输入数据没有被标记,也没有确定的结果.样本数据类别未知,需要根据样本间的相似性对样本集进行分类,试图使类内差距最小化,…

原文地址:http://www.demnag.com/b/java-machine-learning-tools-libraries-cm570/?ref=dzone This is a list of 25 Java Machine learning tools & libraries. Weka has a collection of machine learning algorithms for data mining tasks. The algorithms can either be…

Problems[show] Classification Clustering Regression Anomaly detection Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank…

How to handle Imbalanced Classification Problems in machine learning? from:https://www.analyticsvidhya.com/blog/2017/03/imbalanced-classification-problem/ Introduction If you have spent some time in machine learning and data science, you would have d…