Orange Blog

By: BIOLAB, Apr 30, 2012

Orange GSoC: Multi-Target Learning for Orange

Orange already supports multi-target classification, but the current implementation of clustering trees is written in Python. One of the five projects Orange has chosen at this year’s Google Summer of Code is the implementation of clustering trees in C. The goal of my project is to speed up the building time of clustering trees and lower their spatial complexity, especially when used in random forests. Implementation will be based on Orange’s SimpleTreeLearner and will be integrated with Orange 3.


By: BIOLAB, Feb 2, 2012

New in Orange: Partial least squares regression

Partial least squares regression is a regression technique which supports multiple response variables. PLS regression is very popular in areas such as bioinformatics, chemometrics etc. where the number of observations is usually less than the number of measured variables and where there exists multicollinearity among the predictor variables. In such situations, standard regression techniques would usually fail. The PLS regression is now available in Orange (see documentation)! You can use PLS regression model on single-target or multi-target data sets.

Categories: multitarget pls regression