Linear Regression with multiple variables - Gradient descent in practice I: Feature Scaling 摘要: 本文是吴恩达 (Andrew Ng)老师《机器学习》课程,第五章《多变量线性回归》中第30课时《多元梯度下降法实践 I: 特征缩放》的视频原文字幕。为本人在视频学习过程中记录下来并加以修正,使其更加简洁,方便阅读,以便日...
【Machine Learning】4 多变量线性回归(Linear Regression with Multiple Variables),程序员大本营,技术文章内容聚合第一站。
#加载数据 defload_exdata(filename):data=[]withopen(filename,'r')asf:forlineinf.readlines():line=line.split(',')current=[int(item)foriteminline]#5.5277,9.1302data.append(current)returndata data=load_exdata('ex1data2.txt');data=np.array(data,np.int64)x=data[:,(0,1)].reshape((-1,...
(一)单变量线性回归 Linear Regression with One Variable (二)多变量线性回归 Linear Regression with Multiple Variables (三)逻辑回归 Logistic Regression (四)正则化与过拟合问题 Regularization/The Problem of Overfitting (五)神经网络的表示 Neural Networks:Representation (六)神经网络的学习 Neural Networks:Lear...
Mathematically, it's the same as simple linear regression, and is usually fit using the same cost function, but with more features.Rather than modeling a single relationship, this technique simultaneously models multiple relationships, which it treats as independent of one another. For exampl...
R provides comprehensive support for multiple linear regression. The topics below are provided in order of increasing complexity. Fitting the Model # Multiple Linear Regression Examplefit<-lm(y~x1+x2+x3,data=mydata)summary(fit)# show results ...
Multiple linear regression (MLR) is a statistical technique that uses several explanatory variables to predict the outcome of a response variable.
Perform multiple linear regression with alpha = 0.01. [~,~,r,rint] = regress(y,X,0.01); Diagnose outliers by finding the residual intervalsrintthat do not contain 0. contain0 = (rint(:,1)<0 & rint(:,2)>0); idx = find(contain0==false) ...
multiple要注意区分,是multiple linear regression,还是multiple testing。 前者是说线性回归的变量有多个,后者是说要做多个线性回归,也就是多个检验。 P133,这是第二次作业,考察多重线性回归。这个youtube频道真是精品,用R做统计。这里是R代码的总结。 连续变量和类别型变量总要分开讨论; ...
If x_2=1 , the regression model is y_i=(\beta_0+\beta_2)+(\beta_1+\beta_3)x_{i1}+\epsilon_i . Therefore, the effect of x_1 on the mean of response depends on the value of x_2 . Next, we may be interested in two hypothesis testing problems: How to test whether the ...