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2015-12-14
R
Logarithmic Loss, or simply Log Loss, is a classification loss function often used as an evaluation metric in Kaggle competitions. Since success in these competitions hinges on effectively minimising the Log Loss, it makes sense to have some understanding of how this metric is calculated and how it should be interpreted.
Log Loss quantifies the accuracy of a classifier by penalising false classifications. Minimising the Log Loss is basically equivalent to maximising the accuracy of the classifier, but there is a subtle twist which we’ll get to in a moment.
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2015-12-09
R
Python
2015-12-04
R
The recently published 2015 Data Science Salary Survey conducted by O’Reilly takes a look at the salaries received, tools used and other interesting facts about Data Scientists around the World. It’s based on a survey of over 600 respondents from a variety of industries. The entire report is well worth a read, but I’ve picked out some highlights below.
The majority (67%) of the respondents in the survey were from the United States.
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2015-11-23
2015-11-16
Last week I took a high level look at the trends in children’s names over the last century. Today I’ll dig a little deeper and examine the ebb and flow in popularity of some specific names.
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2015-11-16
2015-11-12
R
I spent a decent chunk of my morning trying to figure out how to construct a sparse adjacency matrix for use with graph.adjacency(). I’d have thought that this would be rather straight forward, but I tripped over a few subtle issues with the Matrix package. My biggest problem (which in retrospect seems rather trivial) was that elements in my adjacency matrix were occupied by the pipe symbol.
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2015-11-09
In light of recent developments, a bit of work that I did almost two years ago has become rather relevant.
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2015-11-06
R
I’ve just been looking at the historical relationship between the London Interbank Offered Rate (LIBOR) and government bond yields. LIBOR data can be found at Quandl and comes in CSV format, so it’s pretty simple to digest. The bond data can be sourced from the US Department of the Treasury. It comes as XML and requires a little more work.
> treasury.xml = xmlParse('data/treasury-yield.xml') > xml.field = function(name) { + xpathSApply(xmlRoot(treasury.
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2015-11-03
Kelsey Jones of Search Engine Journal interviews Guy Kawasaki of Canva. The key take-home message is that maintaining a personal brand is vital even if you are permanently employed. Specifically, it’s important to keep a visible record of who you have worked for and your personal successes.
I'm living proof. I did one thing right for Apple thirty years ago. I've been coasting ever since. Just need to do one thing really right.
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2015-10-30
Julia
2015-10-26
Julia
2015-10-26
Data Scientists are often among the best educated and most experienced on a team. Are you getting the respect you deserve?
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2015-10-20
Git
2015-10-19
Julia
2015-10-16
And even that insanely curious data scientist, if he or she insists on working alone, won’t be able to produce the most valuable insights. Those come from high-performing teams combining individuals who are individually curious and naturally creative, but also collaborative in their approach to the art and science of experimentation. A great data science team is like a jazz quartet, where individuals are always riffing off of one another, and each takes the music to a new and unexpected place.
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2015-10-16
The underscores are invisible in the code that I’m displaying on WordPress using the SyntaxHighlighter Evolved. After a bit of research I found that this was due to the line height being set too small.
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2015-10-15
R
Python
Book Review
2015-10-15
Julia
2015-10-13
Julia
2015-10-13
Julia
I work on a Windows machine behind a corporate firewall during the day. It’s not optimal, but @rlnel kindly passed this information on to me, which has made the situation far more tolerable.
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2015-10-12
Julia
The recipe below works for Light Table v. 0.7.2 and Julia v. 0.4.0. It might work for other versions too, but these are the ones I can vouch for.
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2015-10-12
Julia
2015-10-09
Julia
2015-10-08
Julia
2015-10-07
Julia
2015-10-07
R
Machine Learning
Book Review
2015-10-06
Julia
2015-10-05
Julia
2015-10-02
Julia