# 352 search results for "evaluation"

## Evaluating model performance – A practical example of the effects of overfitting and data size on prediction

May 3, 2014
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Following my last post on decision making trees and machine learning, where I presented some tips gathered from the "Pragmatic Programming Techniques" blog, I have again been impressed by its clear presentation of strategies regarding the evaluation of model performance. I have seen some of these topics presented elsewhere -...

May 2, 2014
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Test coverage of the 10 most downloaded R packages 2014-04-30 Source Introduction How do you know that your code is well tested ? The test coverage is the proportion of source code lines that are executed (covered) when running the tests. It is useful to find the parts of your code that are no exercised no matter how...

## A bit of the agenda of Practical Data Science with R

May 1, 2014
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The goal of Zumel/Mount: Practical Data Science with R is to teach, through guided practice, the skills of a data scientist. We define a data scientist as the person who organizes client input, data, infrastructure, statistics, mathematics and machine learning to deploy useful predictive models into production. Our plan to teach is to: Order the Related posts:

## Example of linear regression and regularization in R

April 28, 2014
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When getting started in machine learning, it's often helpful to see a worked example of a real-world problem from start to finish. But it can be hard to find an example with the "right" level of complexity for a novice. Here's what I look for: uses r...

## Twitter sentiment analysis with R

Recently I designed a relatively simple code in R to analyze the content of Twitter posts by using the categories identified as positive, negative and neutral. The idea of processing tweets is based on a presentation http://www.slideshare.net/ajayohri/twitter-analysis-by-kaify-rais. The algorithm evaluates tweets based on the number of positive and negative words in the tweet. The words in the tweet correspond with the words... Read More »

## Stats in bed, part 1: Ubuntu Touch

April 25, 2014
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Round at the RSS Statistical Computing committee, we were having a chuckle at the prospect of a meeting about Stats In Bed. By which I mean analysis on mobile devices, phones and tablets (henceforth phablets), not some sort of raunchy … Continue reading →

## You don’t need to understand pointers to program using R

April 1, 2014
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R is a statistical analysis package based on writing short scripts or programs (versus being based on GUIs like spreadsheets or directed workflow editors). I say “writing short scripts” because R’s programming language (itself called S) is a bit of an oddity that you really wouldn’t be using except it gives you access to superior Related posts:

## Process and observation uncertainty explained with R

March 31, 2014
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$Process and observation uncertainty explained with R$

Once up on a time I had grand ambitions of writing blog posts outlining all of the examples in the Ecological Detective.1 A few years ago I participated in a graduate seminar series where we went through many of the examples in this book. I am not a population biologist by trade but many of

## Bayesian Data Analysis [BDA3 - part #2]

March 30, 2014
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Here is the second part of my review of Gelman et al.’ Bayesian Data Analysis (third edition): “When an iterative simulation algorithm is “tuned” (…) the iterations will not in general converge to the target distribution.” (p.297) Part III covers advanced computation, obviously including MCMC but also model approximations like variational Bayes and expectation propagation

## Filtering Data with L1 Regularisation

March 27, 2014
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$Filtering Data with L1 Regularisation$

A few days ago I posted about Filtering Data with L2 Regularisation. Today I am going to explore the other filtering technique described in the paper by Tung-Lam Dao. This is similar to the filter discussed in my previous post, but uses a slightly different objective function: where the regularisation term now employs the L1