Posts Tagged ‘ statistics ’

My New Book: Developing, Deploying and Debugging Multi-Armed Bandit Algorithms

July 28, 2012
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I’m happy to announce that I’ve started writing a new book for O’Reilly, which will focus on teaching readers how to use Multi-Armed Bandit Algorithms to build better websites. My hope is that the book can help web developers build up an intuition for the core conundrum facing anyone who wants to build a successful

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Learning R has really made me appreciate SAS

July 25, 2012
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For the past 18 months, it seems like all I’ve heard about in the digital marketing industry is “big data”, and with that, mentions of using Hadoop and R to solve these sorts of problems.  Why are these tools the … Continue reading →Learning R has really made me appreciate SAS is an article from randyzwitch.com,...

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Modeling Trick: Impact Coding of Categorical Variables with Many Levels

July 23, 2012
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Modeling Trick: Impact Coding of Categorical Variables with Many Levels

One of the shortcomings of regression (both linear and logistic) is that it doesn’t handle categorical variables with a very large number of possible values (for example, postal codes). You can get around this, of course, by going to another modeling technique, such as Naive Bayes; however, you lose some of the advantages of regression Related posts:

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Automatic Hyperparameter Tuning Methods

July 20, 2012
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At MSR this week, we had two very good talks on algorithmic methods for tuning the hyperparameters of machine learning models. Selecting appropriate settings for hyperparameters is a constant problem in machine learning, which is somewhat surprising given how much expertise the machine learning community has in optimization theory. I suspect there’s interesting psychological and

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Course at Monash (#2)

July 19, 2012
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Course at Monash (#2)

Here are the slides for the second day of my course at Monash University, Melbourne, in the Special Lectures in Econometrics, with a strong strong similarity with the slides of my course in Roma this Spring. (Ah, sunny Roma…) The first day lecture was very well attended and I hope this remains true for the

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Course at Monash (#1)

July 18, 2012
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Course at Monash (#1)

Here are the slides for the first day of my course at Monash University, Melbourne, in the Special Lectures in Econometrics, with a strong similarity with the slides of my course in Wharton, two years ago. (Be sure to check slide 67! If the update on slideshare works from my flat in Melbourne…) Filed under:

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Criticism 5 of NHST: p-Values Measure Effort, Not Truth

July 17, 2012
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Criticism 5 of NHST: p-Values Measure Effort, Not Truth

Introduction In the third installment of my series of criticisms of NHST, I focused on the notion that a p-value is nothing more than a one-dimensional representation of a two-dimensional space in which (1) the measured size of an effect and (2) the precision of this measurement have been combined in such a way that

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introduction to R: learning by doing (part 2: plots)

July 10, 2012
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introduction to R: learning by doing (part 2: plots)

Lets go one with the second part of learning R by doing R (you will find the first part here. As we have used vectors, matrices and loops in the first part, we will concentrate on graphics in this one. but first we will need data to plot: Sometimes you will need several plots in

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simulation, an ubiquitous tool

July 10, 2012
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simulation, an ubiquitous tool

(This article was first published on Xi'an's Og » R, and kindly contributed to R-bloggers) After struggling for quite a walk on that AMSI public lecture talk, and dreading its loss with the problematic Macbook, I managed to complete a first draft last night in Adelaide, downloading a final set of images from the Web...

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Optimization Functions in Julia

July 9, 2012
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Optimization Functions in Julia

Over the last few weeks, I’ve made a concerted effort to develop a basic suite of optimization algorithms for Julia so that Matlab programmers used to using fminunc() and R programmers used to using optim() can start to transition code over to Julia that requires access to simple optimization algorithms like L-BFGS and the Nelder-Mead

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