# 1228 search results for "regression"

## sab-R-metrics Sidetrack: Bubble Plots

March 22, 2011
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While I had mentioned in my last post that I will cover logistic regression in my next post, I decided that a quick interlude in working with bubble plots would be fun. Bubble plots have become pretty popular recently, especially with all of the Visualization Challenges I've seen around the internet (by the way, I...

## Machine Learning Ex2 – Linear Regression

March 22, 2011
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Thanks to this post, I found OpenClassroom. In addition, thanks to Andrew Ng and his lectures, I took my first course in machine learning. These videos are quite easy to follow. Exercise 2 requires implementing gradient descent algorithm to model data with linear regression. Read More: 243 Words Totally

## Machine Learning Ex5.2 – Regularized Logistic Regression

March 20, 2011
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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. Data 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,...

## How to: Binomial regression models in R

March 19, 2011
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$How to: Binomial regression models in R$

Ever wondered how to predict success or failure as a function of other variables? Here's a quick tutorial on binomial regression in R.

## Machine Learning Ex5.1 – Regularized Linear Regression

March 18, 2011
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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. Data 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)

## Canabalt Revisited: Gamma Distributions, Multinomial Distributions and More JAGS Goodness

March 16, 2011
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Introduction Neil Kodner recently got me interested again in analyzing Canabalt scores statistically by writing a great post in which he compared the average scores across iOS devices. Thankfully, Neil’s made his code and data freely available, so I’ve been revising my original analyses using his new data whenever I can find a free minute.

## sab-R-metrics: Brief Sidetrack for Scatterplot Matrices

March 16, 2011
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In my last two posts I talked about Ordinary Least Squares, then extended this discussion to the multiple predictor case and briefly talked about some of the problems that may arise. These problems can include omitted variable bias, heteroskedasticity, non-normality, and multicollinearity. Most of these problems are relatively minor in practice and have easy fixes,...

## sab-R-metrics: Brief Sidetrack for Scatterplot Matrices

March 16, 2011
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In my last two posts I talked about Ordinary Least Squares, then extended this discussion to the multiple predictor case and briefly talked about some of the problems that may arise. These problems can include omitted variable bias, heteroskedasticity, non-normality, and multicollinearity. Most of these problems are relatively minor in practice and have easy fixes,...

## Machine Learning Ex4 – Logistic Regression and Newton’s Method

March 16, 2011
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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...