By: AJDA, Jul 28, 2017
Do you love Orange? Do you think it is the best thing since sliced bread? Want to thank all the developers for their hard work? Nothing says thank you like a fresh supply of ice cream and now you can help us stock our fridge with your generous donations. 🍦🍦🍦 Donate Support open source software and the team behind Orange. We promise to squander all your contributions purely on ice cream.
By: AJDA, Jul 14, 2017
Orange has a new friend! It’s Miniconda, Anaconda’s little sister. For a long time, the idea was to utilize the friendly nature of Miniconda to install Orange dependencies, which often misbehaved on some platforms. Miniconda provides Orange with Python 3.6 and conda installer, which is then used to handle everything Orange needs for proper functioning. So sssssss-mooth! Miniconda Installer Please know that our Miniconda installer is in a beta state, but we are inviting adventurous testers to try it and report any bugs they find to our issue tracker [there won’t be any of course!
By: AJDA, Jun 19, 2017
In data mining, preprocessing is key. And in text mining, it is the key and the door. In other words, it’s the most vital step in the analysis. Related: Text Mining add-on So what does preprocessing do? Let’s have a look at an example. Place Corpus widget from Text add-on on the canvas. Open it and load Grimm-tales-selected. As always, first have a quick glance of the data in Corpus Viewer.
By: AJDA, Jun 9, 2017
Yesterday was no ordinary day at the Faculty of Computer and Information Science, University of Ljubljana - there was an unusually high proportion of Social Sciences students, researchers and other professionals in our classrooms. It was all because of a Text Analysis for Social Scientists workshop. Related: Data Mining for Political Scientists Text mining is becoming a popular method across sciences and it was time to showcase what it (and Orange) can do.
By: AJDA, Jun 5, 2017
One more exciting visualization has been introduced to Orange - a Nomogram. In general, nomograms are graphical devices that can approximate the calculation of some function. A Nomogram widget in Orange visualizes Logistic Regression and Naive Bayes classification models, and compute the class probabilities given a set of attributes values. In the nomogram, we can check how changing of the attribute values affect the class probabilities, and since the widget (like widgets in Orange) is interactive, we can do this on the fly.
By: BLAZ, Apr 25, 2017
Say I am given a collection of images of traffic signs, and would like to find which signs stick out. That is, which traffic signs look substantially different from the others. I would assume that the traffic signs are not equally important and that some were designed to be noted before the others. I have assembled a small set of regulatory and warning traffic signs and stored the references to their images in a traffic-signs-w.
By: AJDA, Apr 7, 2017
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, Apr 3, 2017
Data does not always come in a nice tabular form. It can also be a collection of text, audio recordings, video materials or even images. However, computers can only work with numbers, so for any data mining, we need to transform such unstructured data into a vector representation. For retrieving numbers from unstructured data, Orange can use deep network embedders. We have just started to include various embedders in Orange, and for now, they are available for text and images.
By: AJDA, Mar 17, 2017
k-Means is one of the most popular unsupervised learning algorithms for finding interesting groups in our data. It can be useful in customer segmentation, finding gene families, determining document types, improving human resource management and so on. But… have you ever wondered how k-means works? In the following three videos we explain how to construct a data analysis workflow using k-means, how k-means works, how to find a good k value and how silhouette score can help us find the inliers and the outliers.
By: AJDA, Mar 9, 2017
Why is Orange so great? Because it helps people solve problems quickly and efficiently. Sašo Jakljevič, a former student of the Faculty of Computer and Information Science at University of Ljubljana, created the following motivational videos for his graduation thesis. He used two belowed datasets, iris and zoo, to showcase how to tackle real-life problems with Orange.