测试数据及源码下载:http://pan.baidu.com/s/1mgiIVm4 OneVariable.java 1packageOneVariableVersion;23importjava.io.IOException;4importjava.util.List;567/**8* Linear Regression with One Variable9*@authorXBW10* @date 2014年8月
1function J =computeCostMulti(X, y, theta)2%COMPUTECOSTMULTI Compute costforlinear regression with multiple variables3% J = COMPUTECOSTMULTI(X, y, theta) computes the cost ofusingthetaasthe4% parameterforlinear regression to fit the data pointsinX and y56%Initialize some useful values7m = len...
This equation is a polynomial equation of degree 1, which is the base of linear regression with one variable:Figure 2.2: Representation of the equations y = 2*x + 1, y = x + 1, and y = 0.5*x + 1 on the coordinate system
In this part of this exercise, you will implement linear regression with one variable to predict profits for a food truck. Suppose you are the CEO of a restaurant franchise and are considering different cities for opening a new outlet. The chain already has trucks in various cities and you h...
我们的目标和单变量线性回归问题中一样,是要找出使得代价函数最小的一系列参数。多变量线性回归的批量梯度下降算法为: 求导数后得到: (3)向量化计算 向量化计算可以加快计算速度,怎么转化为向量化计算呢? 在多变量情况下,损失函数可以写为: 对theta求导后得到: ...
Linear regression is one of the most popular modeling techniques because, in addition to explaining the relationship between variables (like correlation), it also gives an equation that can be used to predict the value of a response variable based on a value of the predictor variable. ...
Fitting a model to your data can tell you how one variable increases or decreases as the value of another variable changes. For example, if we have a dataset of houses that includes both their size and selling price, a regression model can help quantify the relationship between the two. (...
Linear regression 属于supervised learning. Notation: x(i):输入-input variables, also called input features. y(i):输出-output variable, also called target vatiable that we are trying to predict. (x(i),y(i)):数据对-called a training example. ...
In many polynomial regression models, adding terms to the equation increases both R2and adjusted R2. In the preceding example, using a cubic fit increased both statistics compared to a linear fit. (You can compute adjusted R2for the linear fit for yourself to demonstrate that it has a lower...
6 areg — Linear regression with a large dummy-variable set Assume that we were to collect data by randomly sampling 10,000 doctors (from 100 hospitals) and then sampling 10 patients of each doctor, yielding a total dataset of 100,000 patients in a cluster sample. If in some regression ...