Orange Blog

By: Ajda Pretnar Žagar, Aug 4, 2022

Timeseries add-on lost a lot of weight

Timeseries' visualizations are becoming fully PyQt, making them easier to maintain. We've also fixed many bugs, which should make working with the timeseries a joy once again.

By: ANDREJA, Aug 27, 2018

Explaining Kickstarter Success

On Kickstarter most app ideas don’t get funded. But why is that? When we are looking for possible explanations, it is easy to ascribe the failure to the type of the idea. But what about those rare cases, where an app idea gets funded? Can we figure out why a particular idea succeeded? Our new widget Explain Predictions can do just that - explain why they will succeed. Or at least, explain why the classifier thinks they will.

By: AJDA, Apr 7, 2017

Model replaces Classify and Regression

Did you recently wonder where did Classification Tree go? Or what happened to Majority? Orange 3.4.0 introduced a new widget category, Model, which now contains all supervised learning algorithms in one place and replaces the separate Classify and Regression categories. This, however, was not a mere cosmetic change to the widget hierarchy. We wanted to simplify the interface for new users and make finding an appropriate learning algorithm easier. Moreover, now you can reuse some workflows on different data sets, say housing.

By: AJDA, Nov 30, 2016

Data Mining for Political Scientists

Being a political scientist, I did not even hear about data mining before I’ve joined Biolab. And naturally, as with all good things, data mining started to grow on me. Give me some data, connect a bunch of widgets and see the magic happen! But hold on! There are still many social scientists out there who haven’t yet heard about the wonderful world of data mining, text mining and machine learning.

By: BLAZ, Apr 25, 2012

Orange team wins JRS 2012 Data Mining Competition

Lead by Jure Žbontar, the team from University of Ljubljana wins over 126 other entrants in an international competition in predictive data analytics. Jure’s team consisted of several Orange developers and computer science students: Miha Zidar, Blaž Zupan, Gregor Majcen, Marinka Žitnik in Matic Potočnik. To win, the team had to predict topics for 10.000 MedLine documents that were represented with over 25.000 algorithmically derived numerical features. Given was training set of another 10.

Categories: competition prediction