Orange Blog
By: AJDA, May 20, 2016
Oasys: Orange Canvas applied to Optical Physics
This week we’re hosting experts in optical physics from Elettra Sincrotrone Trieste and European Synchrotron Radiation Facility in our laboratory. For a long time they have been interested in developing a user interface that integrates different simulation tools and data analysis software within one environment. It all came true with Orange Canvas and the OASYS system. We’ve already written about this two years ago, when the idea first came up. Now the actual software is ready and is being used by researchers for everyday analysis and prototyping.
By: AJDA, Apr 25, 2016
Association Rules in Orange
Orange is welcoming back one of its more exciting add-ons: Associate! Association rules can help the user quickly and simply discover the underlying relationships and connections between data instances. Yeah! The add-on currently has two widgets: one for Association Rules and the other for Frequent Itemsets. With Frequent Itemsets we first check frequency of items and itemsets in our transaction matrix. This tell us which items (products) and itemsets are the most frequent in our data, so it would make a lot of sense focusing on these products.
By: AJDA, Apr 14, 2016
Univariate GSoC Success
Google Summer of Code application period has come to an end. We’ve received 34 applications, some of which were of truly high quality. Now it’s upon us to select the top performing candidates, but before that we wanted to have an overlook of the candidate pool. We’ve gathered data from our Google Form application and gave it a quick view in Orange. First, we needed to preprocess the data a bit, since it came in a messy form of strings.
By: AJDA, Apr 1, 2016
Version 3.3.1 - Updates and Features
About a week ago we issued an updated stable release of Orange, version 3.3.1. We’ve introduced some new functionalities and improved a few old ones. Here’s what’s new in this release: New widgets: Distance Matrix for visualizing distance measures in a matrix, Distance Transformation for normalization and inversion of distance matrix, Save Distance Matrix and Distance File for saving and loading distances. Last week we also mentioned a really amazing Silhouette Plot, which helps you visually assess cluster quality.
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, Mar 12, 2016
Overfitting and Regularization
A week ago I used Orange to explain the effects of regularization. This was the second lecture in the Data Mining class, the first one was on linear regression. My introduction to the benefits of regularization used a simple data set with a single input attribute and a continuous class. I drew a data set in Orange, and then used Polynomial Regression widget (from Prototypes add-on) to plot the linear fit.
By: AJDA, Mar 3, 2016
Orange at Google Summer of Code 2016
Orange team is extremely excited to be a part of this year’s Google Summer of Code! GSoC is a great opportunity for students around the world to spend their summer contributing to an open-source software, gaining experience and earning money. Accepted students will help us develop Orange (or other chosen OSS project) from May to August. Each student is expected to select and define a project of his/her interest and will be ascribed a mentor to guide him/her through the entire process.
By: AJDA, Feb 26, 2016
Getting Started Series: Part Two
We’ve recently published two more videos in our Getting Started with Orange series. The series is intended to introduce beginners to Orange and teach them how to use its components. The first video explains how to do hierarchical clustering and select interesting subsets directly in Orange: while the second video introduces classification trees and predictive modelling: The seventh video in the series will address how to score classification and regression models by different evaluation methods.
By: AJDA, Jan 29, 2016
Tips and Tricks for Data Preparation
Probably the most crucial step in your data analysis is purging and cleaning your data. Here are a couple of cool tricks that will make your data preparation a bit easier. Use a smart text editor. We can recommend Sublime Text as it an extremely versatile editor that supports a broad variety of programming languages and markups, but there are other great tools out there as well. One of the best things you’ll keep coming back to in your editor is ‘Replace’ function that allows you to replace specified values with different ones.
By: AJDA, Jan 22, 2016
Making Predictions
One of the cool things about being a data scientist is being able to predict. That is, predict before we know the actual outcome. I am not talking about verifying your favorite classification algorithm here, and I am not talking about cross-validation or classification accuracies or AUC or anything like that. I am talking about the good old prediction. This is where our very own Predictions widget comes to help.