# 324 search results for "evaluation"

## Beyond R, or on the Hunt for New Tools

May 12, 2014
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For more than four years now (judging by the first post on my old blog), R has been my primary tool for market research. It has thought me a lot, and it has helped me to me advance smoothly in the field of semi-automated trading. Lately however I started realizing that R is lacking essential

## Customer Satisfaction and Loyalty: Structural Equation Model or One-Dimensional Dissonance

May 9, 2014
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Causal thinking is seductive. Product experience comes first, then feelings of satisfaction, and finally intentions to continue as a customer. Although customer satisfaction and loyalty data tend to be collected all at one time within the same question...

## “The Winner Takes It All” – Tuning and Validating R Recommendation Models Inside Tableau

May 4, 2014
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Introduction My last blog article shows how to build an interactive recommendation engine in Tableau using a simple model utilizing the cosine similarity measure. While this can be a good way to explore unknown data, it is wise to validate any model before...

## 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:

## 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 →

## Detecting bubbles in real time

April 14, 2014
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Recently, we hear a lot about a housing bubble forming in UK. Would be great if we would have a formal test for identifying a bubble evolving in real time, I am not familiar with any such test. However, we … Continue reading → Related posts: Volatility forecast evaluation in R In portfolio...

## 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