Archive for March, 2010

scikits.learn 0.2 release

Monday, March 22nd, 2010

Today I released a new version of the scikits.learn library for machine learning.
This new release includes the new libsvm bindings, Jake VanderPlas’ BallTree algorithm for *fast* nearest neighbor queries in high dimension, etc. Here is the official announcement.
As usual, it can be downloaded from sourceforge or from the PyPI.

Plot the maximum margin hyperplane with scikits.learn

Wednesday, March 17th, 2010

Suppose some given data points each belong to one of two classes, and the goal is to decide which class a new data point will be in. In the case of support vector machines, a data point is viewed as a p-dimensional vector (2-dimensional in this example), and we want to know whether we can […]

Fast bindings for LibSVM in scikits.learn

Tuesday, March 9th, 2010

LibSVM is a C++ library that implements several Support Vector Machine algorithms that are commonly used in machine learning. It is a fast library that has no dependencies and most machine learning frameworks bind it in some way or another. LibSVM comes with a Python interface written in swig, but this interface is inherently slow […]

scikits.learn coding sprint in Paris

Thursday, March 4th, 2010

Yesterday we had an extremely productive coding sprint for the scikits.learn. The idea was to put people with common interests in a room and make them work in a single codebase.
Alexandre Gramfort and Olivier Grisel worked on GLMNet, Bertrand Thirion and Gaël Varoquaux worked on univariate feature selection and Vincent worked on Bayesian Regression.

I […]