this is going to be my overall objective function for linear regression. And just to, you know rewrite this out a little bit more cleanly, what I'm going to do by convention
平方误差的思想就是将实际样本数据值与我们拟合出的线做对应差值,即计算差距 程序需要一个机制去评估我们θ是否比较好,所以说需要对我们做出的h函数进行评估,一般这个函数称为损失函数(loss function)或者错误函数(error function) 要求出theta,使代价最小,即代表我们拟合出来的方程距离真实值最近。前面乘上的1/2是为...
1% Compute Costforlinear regression2%cost Function函数实现___利用矩阵操作进行!!3function J =computeCost(X, y, theta)45%Initialize some useful values6m = length(y); %number of training examples7J =0;89%Instructions: Compute the cost of a particular choice of theta10% You shouldsetJ to the...
1% Compute Costforlinear regression2%cost Function函数实现___利用矩阵操作进行!!3function J =computeCost(X, y, theta)45%Initialize some useful values6m = length(y); %number of training examples7J =0;89%Instructions: Compute the cost of a particular choice of theta10% You shouldsetJ to the...
吴恩达(详解机器学习)【中英字幕】-08-单变量线性回归成本函数Cost function Intution 19播放 吴恩达(详解机器学习)【中英字幕】-07-成本函数Cost function 49播放 吴恩达(详解机器学习)【中英字幕】-06-线性回归Linear regression Part2 8播放 吴恩达(详解机器学习)【中英字幕】-05-线性回顾归Linear regression Part1...
$J(\theta_0, \theta_1)$被成为代价函数(cost function),这是回归问题中最常使用的方法. 现在要做的就是得到使 $J(\theta_0, \theta_1)$ 最小的 $\theta_0$ 和 $\theta_1$ 最小化$J(\theta_0, \theta_1)$ 为了更好的理解最小化的过程,先假设 $\theta_0$ = 0,这样就简化了预测函数$h...
对于logistic回归来说,模型自然就是logistic回归,策略最常用的方法是用一个损失函数(loss function)或代价函数(cost function)来度量预测错误程度,算法则是求解过程,后期会详细描述相关的优化算法。 logistic函数求导 KaTeX parse error: No such environment: align at position 7: \begin{̲a̲l̲i̲g̲n...
We cannot use the same cost function that we use for linear regression because the Logistic Function will cause the output to be wavy, causing many local optima. In other words, it will not be a convex function. non-convex: convex: ...
The Function used to quantify this loss during the training phase in the form of a single real number is known as “Loss Function”. These are used in those supervised learning algorithms that use optimization techniques. Notable examples of such algorithms are regression,logistic regression, etc....
multiple linear regressionoutliersConsider a times series in simple linear regression. It is shown that under suitable conditions point estimates or predictions for the next time period into the future are unaffected by values of the dependent variable at some given time period in the past. The ...