Orange Blog

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: BLAZ, Dec 22, 2016

The Beauty of Random Forest

It is the time of the year when we adore Christmas trees. But these are not the only trees we, at Orange team, think about. In fact, through almost life-long professional deformation of being a data scientist, when I think about trees I would often think about classification and regression trees. And they can be beautiful as well. Not only for their elegance in explaining the hidden patterns, but aesthetically, when rendered in Orange.


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: PRIMOZGODEC, Aug 16, 2016

Visualization of Classification Probabilities

This is a guest blog from the Google Summer of Code project. Polynomial Classification widget is implemented as a part of my Google Summer of Code project along with other widgets in educational add-on (see my previous blog). It visualizes probabilities for two-class classification (target vs. rest) using color gradient and contour lines, and it can do so for any Orange learner. Here is an example workflow. The data comes from the File widget.


By: MATEVZKREN, Aug 5, 2016

Rule Induction (Part I - Scripting)

This is a guest blog from the Google Summer of Code project. We’ve all heard the saying, “Rules are meant to be broken.” Regardless of how you might feel about the idea, one thing is certain. Rules must first be learnt. My 2016 Google Summer of Code project revolves around doing just that. I am developing classification rule induction techniques for Orange, and here describing the code currently available in the pull request and that will become part of official distribution in an upcoming release 3.


By: AJDA, Jul 29, 2016

Pythagorean Trees and Forests

Classification Trees are great, but how about when they overgrow even your 27'' screen? Can we make the tree fit snugly onto the screen and still tell the whole story? Well, yes we can. Pythagorean Tree widget will show you the same information as Classification Tree, but way more concisely. Pythagorean Trees represent nodes with squares whose size is proportionate to the number of covered training instances. Once the data is split into two subsets, the corresponding new squares form a right triangle on top of the parent square.


By: AJDA, Jul 5, 2016

Rehaul of Text Mining Add-On

Google Summer of Code is progressing nicely and some major improvements are already live! Our students have been working hard and today we’re thanking Alexey for his work on Text Mining add-on. Two major tasks before the midterms were to introduce Twitter widget and rehaul Preprocess Text. Twitter widget was designed to be a part of our summer school program and it worked beautifully. We’ve introduced youngsters to the world of data mining through social networks and one of the most exciting things was to see whether we can predict the author from the tweet content.


By: AJDA, Mar 23, 2016

All I See is Silhouette

Silhouette plot is such a nice method for visually assessing cluster quality and the degree of cluster membership that we simply couldn’t wait to get it into Orange3. And now we did. What this visualization displays is the average distance between instances within the cluster and instances in the nearest cluster. For a given data instance, the silhouette close to 1 indicates that the data instance is close to the center of the cluster.


By: BLAZ, Dec 19, 2015

Model-Based Feature Scoring

Feature scoring and ranking can help in understanding the data in supervised settings. Orange includes a number of standard feature scoring procedures one can access in the Rank widget. Moreover, a number of modeling techniques, like linear or logistic regression, can rank features explicitly through assignment of weights. Trained models like random forests have their own methods for feature scoring. Models inferred by these modeling techniques depend on their parameters, like type and level of regularization for logistic regression.


By: AJDA, Oct 16, 2015

Learners in Python

We’ve already written about classifying instances in Python. However, it’s always nice to have a comprehensive list of classifiers and a step-by-step procedure at hand. TRAINING THE CLASSIFIER We start with simply importing Orange module into Python and loading our data set. >>>> import Orange >>>> data = Orange.data.Table("titanic") We are using ‘titanic.tab’ data. You can load any data set you want, but it does have to have a categorical class variable (for numeric targets use regression).