其中E(\mathbf{w})也称作代价函数 (cost function),引入因⼦\frac {1}{2}是为了简化一阶导数形式,在本文第三节将从概率的角度进一步解释。 2.线性基函数模型(Linear Basis Function Models) 由上文可知,当我们引入一些输⼊变量x的幂函数进⾏线性组合时,对于很多非线性实际应用的拟合效果会更好,但是精确...
for most regression problems. There are other cost functions that will work pretty well, but the squared error cost function is probably the most common used one for regression problems. Later in this class we'll also talk about alternative cost functions as well, but this ...
1 % Compute Cost for linear regression 2 % cost Function函数实现___利用矩阵操作进行!! 3 function J = computeCost(X, y, theta) 4 5 % Initialize some useful values 6 m = length(y); % number of training examples 7 J = 0; 8 9 % Instructions: Compute the cost of a particular cho...
2. Multiple Linear Regression Multiple regression is similar to linear regression, but it includes more than one independent value, implying that we attempt to predict a value based on two or more variables. 3. Polynomial Regression Polynomial regression is a type of regression analysis that uses ...
斯坦福CS229监督学习的第一课——线性回归。在看之前觉得线性回归这么熟悉而又简单,似乎没有必要细看。真正看的时候感觉里面还是包含了一些ML的基本方法和拓展算法的,于是决定写一写。 Linear Regression Overvi…
#Locally Weighted Linear Regression 局部加权回归(非参数学习方法) ##x为数据矩阵(mxn m:样本数 n:特征数 );y观测值(mx1);xp为需要预测的样本特征,t权重函数的权值变化速率 #error终止条件,相邻两次搜索结果的幅度; #step为设定的固定步长;maxiter最大迭代次数,alpha,beta为回溯下降法的参数 ...
1 %绘制拟合曲线2%Plot the linear fit3hold on; %keep previous plot visible4plot(X(:,2), X*theta,'-')5legend('Training data','Linear regression') %添加图例6hold off % don't overlay any more plots on this figure 1. 2. 3.
Example 1: Linear regression can predict house prices based on size. For example, if the formula is: Price = 50,000 + 100 × Size (sq. ft), a 2,000 sq. ft. house would cost: Price = 50,000 + 100 × 2,000 = 250,000. It helps find relationships and make predictions. Example...
机器学习02 Linear regression 机器学习100天 day02 Linear regression 注意:1、fit中输入自变量和因变量,需要都是array类型,如果是Series需要将其进行转换,X_train = np.array(X_train); trainX_train = X_train.reshape(len(X_train),1),转换后会是shape会是(len(X_train),1) regressor = l......
Linear regression formula ŷ is the value we are predicting. n is the number of features of our data points. xi is the value of the ith feature. Θi are the parameters of the model, where Θ0 is the bias term. All the other parameters are the weights for the features of our ...