Blog Archives

Yet Another Caret Workshop

Yet Another Caret Workshop

IntroYesterday I gave a workshop on applied predictive modelling with caret at the 1st LSE Computational Social Science hackathon. Organiser privileges. I put together some introductory code and started a simple GitHub repo for the participants, so I thought I’d share it here as well. This is not supposed to cover all aspects of caret (plus there is already...

Read more »

1st LSE CSS Hackathon! London 17-19 April

1st LSE CSS Hackathon! London 17-19 April

It’s Happening, Folks!Whew! Less than a month left and I still haven’t publicised this on my blog. People, we are hosting the 1st Computational Social Science Hackathon at the London School of Economics and you are invited!It’s completely free an...

Read more »

1st LSE CSS Hackathon! London 17-19 April

1st LSE CSS Hackathon! London 17-19 April

It’s Happening, Folks!Whew! Less than a month left and I still haven’t publicised this on my blog. People, we are hosting the 1st Computational Social Science Hackathon at the London School of Economics and you are invited!It’s completely free an...

Read more »

Supervised vs. Unsupervised Learning: Exploring Brexit with PLS and PCA

February 13, 2018
By
Supervised vs. Unsupervised Learning: Exploring Brexit with PLS and PCA

Outcome SupervisionYesterday I was part of an introductory session on machine learning and unsurprisingly, the issue of supervised vs. unsupervised learning came up. In social sciences, there is a definite tendency for the former; there is more or less always a target outcome or measure that we want to optimise the performance of our models for. This reminded me of...

Read more »

Supervised vs. Unsupervised Learning: Exploring Brexit with PLS and PCA

February 13, 2018
By
Supervised vs. Unsupervised Learning: Exploring Brexit with PLS and PCA

Outcome Supervision Yesterday I was part of an introductory session on machine learning and unsurprisingly, the issue of supervised vs. unsupervised learning came up. In social sciences, there is a definite tendency for the former; there is more or less always a target outcome or measure that we want to optimise the performance of our models for. This reminded me of...

Read more »

Scraping Wikipedia Tables from Lists for Visualisation

January 29, 2018
By
Scraping Wikipedia Tables from Lists for Visualisation

Get WikiTables from Lists Recently I was asked to submit a short take-home challenge and I thought what better excuse for writing a quick blog post! It was on short notice so initially I stayed within the confines of my comfort zone and went for something safe and bland. However, I alleviated that rather fast; I guess you want to...

Read more »

Scraping Wikipedia Tables from Lists for Visualisation

January 29, 2018
By
Scraping Wikipedia Tables from Lists for Visualisation

Get WikiTables from Lists Recently I was asked to submit a short take-home challenge and I thought what better excuse for writing a quick blog post! It was on short notice so initially I stayed within the confines of my comfort zone and went for something safe and bland. However, I alleviated that rather fast; I guess you want to...

Read more »

Predicting Conflict Duration with (gg)plots using Keras

January 21, 2018
By
Predicting Conflict Duration with (gg)plots using Keras

An Unlikely Pairing Last week, Marc Cohen from Google Cloud was on campus to give a hands-on workshop on image classification using TensorFlow. Consequently, I spent most of my time thinking about how I can incorporate image classifiers in my work. As my research is primarily on forecasting armed conflict duration, it’s not really straightforward to make a connection between...

Read more »

Quantitative Story Telling with Shiny: Gender Bias in Syllabi

Quantitative Story Telling with Shiny: Gender Bias in Syllabi

LSE IR Gender and Diversity ProjectTwo shinydashboard posts in a row, that’s a first. As I mentioned on Twitter, I’m not really this productive; rather, the apps had been on the proverbial shelf for a while and I’m just releasing them now. In fac...

Read more »

Visualising US Voting Records with shinydashboard

December 26, 2017
By
Visualising US Voting Records with shinydashboard

Introducing adavis My second ever post on this blog was on introducing adamap, a Shiny app that maps Americans for Democratic Action voting scores (the so-called Liberal Quotient) between 1947-2015. It was built with highcharter, and hence it was nicely interactive but quite slow. I wanted to switch to another package since, and when I eventually ran into statebins, I...

Read more »

Search R-bloggers


Sponsors

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)