In previous Monitoring Productivity Experiment post I looked into the hours I spent in computer, now will look into the hours Others spend in computer, which is far more interesting :) To find things like what day people spend more time on computer, ho...
Exercise 5.2 Improves the Logistic Regression implementation done in Exercise 4 by adding a regularization parameter that reduces the problem of over-fitting. We will be using Newton's Method.
Here's the data we want to fit.
# linear regression # load the data mydata = read.csv("http://spreadsheets.google.com/pub?key=0AnypY27pPCJydHZPN2pFbkZGd1RKeU81OFY3ZHJldWc&output=csv", header = TRUE) # plot the data plot(mydata$u, mydata$v,, xlab="u", ylab="v") points(mydata$u,...
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.
Here'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)
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...
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.
As 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 ...
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...
Got for 2010 Xmas a very cool book called the "4 Hour Body"(thanks Jose Santos) written by Tim Ferriss who write a previous favorite of mine about productivity, the 4 hour work week. Its an interesting book, because it has a scientific approach, it ...
For over a year now, i've been collecting how much time i spend in computer and how much of it is actually used in creative/productive activities. By productive activity i mean that the time spent in text editor(emacs), terminal, excel or a datab...
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
