因为先验服从均匀分布,所以求鲁棒线性回归即求Laplace/Student最大似然。在heavy tail(奇异点较多)情况下用鲁棒线性回归,因为Laplace/Student分布比高斯分布更鲁棒。 似然函数为: 由于零点不可微,所以求解析解困难,无法使用梯度下降法。引入Huber损失函数解决此问题: 四、Lasso回归(Least absolute shrinkage
课程链接:Machine Learning: Regression | Coursera第一章:Simple Linear Regression 1.领域知识在lR中有什么用?feature extraction的时候需要对这个领域的理解。 2.线性回归的点方程和线方程表示? 3.梯度下降计算loss时是计算所有样本点的loss还是部分点的loss?4.什么是凸函数?5.可以用梯度=0来解LR嘛?可以解其它ML...
"Regression is what scientists and enterprises use when answering quantitative questions, specifically of the type 'how many,' 'how much,' 'when will' and so on. In machine learning, it discovers any measurement that is not currently available in the data," Khadilkar explained. Two common tech...
一、两类Logistic回归(输出值[0,1],预测的同时给出分类的概率,用于二分类) 目标y∈{0,1}服从Bernolli分布: -log似然为: ,其中 (1)求解方法 一阶梯度下降公式: 法1:随机梯度下降: 若u(x),y∈{-1,1},则是著名的Perceptron感知机算法,a为学习率: 法2:二阶梯度下降(牛顿法/切线法) 一阶梯度:将导数...
A complex model is fit based on a measure of fit to the training data plus a measure of overfitting different than that used in ridge. This lasso method has had impact in numerous applied domains, and the ideas behind the method have fundamentally changed machine learning and statistics. You...
tools for machine learning ; experience is important 2.supervised learning “right answers”given supervised learning:数据集中的每个数据都是正确的答案 Regression Question : predict continuous valued output (Regression Question) key : predict ;continuous data;回归问题 ...
Logistic regression is a powerful and interpretable classification algorithm widely used in machine learning. Understanding its sigmoid function, cost function, assumptions, and implementation equips you to apply it effectively in real-world scenarios. If you want to learn about these techniques, then yo...
Machine Learning Experiment5 Regularization(正则化) 详解+代码实现 serverless线性回归 在做线性回归或者逻辑回归的时候,会遇到过拟合问题,即,在训练集上的error很小,但是在测试集上的偏差却很大。因此,引入正则化项,防止过拟合。保证在测试集上获得和在训练集上相同的效果。 种花家的奋斗兔 2020/11/13 3520 Machin...
Linear regression is perhaps one of the most well known and well understood algorithms in statistics and machine learning. In this post you will discover the linear regression algorithm, how it works and how you can best use it in on your machine learning projects. In this post you will lear...
This is where machine learning and its many techniques come into play. In this chapter, we will introduce machine learning as a practice along with some of its most basic and critical techniques such as regression and classification. In doing this, we will also discuss other critical details ...