Posts Tagged ‘ regressionanalysis ’

Machine Learning Ex5.1 – Regularized Linear Regression

March 18, 2011
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Machine Learning Ex5.1 – Regularized Linear Regression

Exercise 5.1 Improves the Linear Regression implementation done in Exercise 3 by adding a regularization parameter that reduces the problem of over-fitting.Over-fitting occurs especially when fitting a high-order polynomial, that we will try to do here.DataHere's the points we will make a model from:# linear regression mydata = read.csv("http://spreadsheets.google.com/pub?hl=en_GB&hl=en_GB&key=0AnypY27pPCJydGhtbUlZekVUQTc0dm5QaXp1YWpSY3c&output=csv", header = TRUE)# view data plot(mydata)http://al3xandr3.github.com/img/ml-ex51-data.png

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Machine Learning Ex4 – Logistic Regression and Newton’s Method

March 16, 2011
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Machine Learning Ex4 – Logistic Regression and Newton’s Method

Exercise 4 is all about using Newton's Method to implement logistic regression on a classification problem.For all this to make sense i suggest having a look at Andrew Ng machine learning lectures on openclassroom.We start with a dataset representing 40 students who were admitted to college and 40 students who were not admitted, and their corresponding...

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Machine Learning Ex3 – multivariate linear regression

March 8, 2011
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Machine Learning Ex3 – multivariate linear regression

Exercise 3 is about multivariate linear regression. First part is about finding a good learning rate (alpha) and 2nd part is about implementing linear regression using normal equations instead of the gradient descent algorithm.DataAs usual hosted in google docs:mydata = read.csv("http://spreadsheets.google.com/pub?key=0AnypY27pPCJydExfUzdtVXZuUWphM19vdVBidnFFSWc&output=csv", header = TRUE)# show last 5 rows tail(mydata, 5)area bedrooms price 43 2567 ...

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Machine Learning Ex2 – linear regression

February 24, 2011
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Machine Learning Ex2 – linear regression

Andrew Ng has posted introductory machine learning lessons on the OpenClassRoom site. I've watched the first set and will here solve Exercise 2.The exercise is to build a linear regression implementation, I'll use R.The point of linear regression is to come up with a mathematical function(model) that represents the data as best as possible, that is done...

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