Archives of #Dataset

PyConUK 2013

I’m just finishing with PyConUK, it has been a fun 3 days (and the sprints carry on tomorrow). Yesterday I presented a lightly tweaked version of my Brand Disambiguation with scikit-learn talk on natural language processing for social media processing. I had 65 people in the room (cripes!), 2/3 had used ML or NLP for […]

Overfitting with a Decision Tree

Below is a plot of Training versus Testing errors using a Precision metric (actually 1.0-precision, so lower is better) that shows how easy it is to over-fit a decision tree to the detriment of generalisation. It is important to check that a classifier isn’t overfitting to the training data such that it is just learning […]

Visualising London, Brighton and the UK using Geo-Tweets

Recently I’ve been grabbing Tweets some some natural language processing analysis (in Python using NetworkX and NLTK) – see this PyCon and PyData conversation analysis. Using the London dataset (visualised in the PyData post) I wondered if the geo-tagged tweets would give a good-looking map of London. It turns out that it does: You can […]

Testing 3 modern face detection libraries (, openCV, libccv)

As a research project months back Balthazar and I tested 3 modern face detection libraries (definitely see Balthazar’s write-up). had just been acquired by facebook, they had a great and free service which annotated not just face locations but also sex, age and emotion. We also tested OpenCV (popular and free) and the lesser […]