Orange Blog
By: AJDA, Dec 11, 2015
Report is back! (and better than ever)
I’m sure you’d agree that reporting your findings when analyzing the data is crucial. Say you have a couple of interesting predictions that you’ve tested with several methods many times and you’d like to share that with the world. Here’s how. Save Graph just got company - a Report button! Report works in most widgets, apart from the very obvious ones that simply transmit or display the data (Python Scripting, Edit Domain, Image Viewer, Predictions…).
By: AJDA, Nov 27, 2015
Mining our own data
Recently we’ve made a short survey that was, upon Orange download, asking people how they found out about Orange, what was their data mining level and where do they work. The main purpose of this is to get a better insight into our user base and to figure out what is the profile of people interested in trying Orange. Here we have some preliminary results that we’ve managed to gather in the past three weeks or so.
By: AJDA, Oct 30, 2015
Ghostbusters
Ok, we’ve just recently stumbled across an interesting article on how to deal with non normal (non-Gaussian distributed) data. We have an absolutely paranormal data set of 20 persons with weight, height, paleness, vengefulness, habitation and age attributes (download). Let’s check the distribution in Distributions widget. Our first attribute is “Weight” and we see a little hump on the left. Otherwise the data would be normally distributed. Ok, so perhaps we have a few children in the data set.
By: AJDA, Oct 19, 2015
SQL for Orange
We bet you’ve always wanted to use your SQL data in Orange, but you might not be quite sure how to do it. Don’t worry, we’re coming to the rescue. The key to SQL files is installation of ‘psycopg2’ library in Python. WINDOWS Go to this website and download psycopg2 package. Once your .whl file has downloaded, go to the file directory and run command prompt. Enter “pip install [file name]” and run it.
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).
By: BLAZ, Oct 9, 2015
Data Mining Course in Houston
We have just completed an Introduction to Data Mining, a graduate course at Baylor College of Medicine in Texas, Houston. The course was given in September and consisted of seven two-hour lectures, each one followed with a homework assignment. The course was attended by about 40 students and some faculty and research staff. This was a challenging course. The audience was new to data mining, and we decided to teach them with the newest, third version of Orange.
By: AJDA, Sep 11, 2015
Hubbing with the Hub widget
So you have painted two data sets and loaded another one from a file, and now you are testing predictions of logistic regression, classification trees and SVM on it? Tired of having to reconnect the Paint data widget and the File widget back and forth whenever you switch between them? Say no more! Look no further! Here is the new Hub widget! Hub widget is the most versatile widget available so far.
By: AJDA, Sep 4, 2015
Updated Widget Documentation
Happy news for all passionate Orange users! We’ve uploaded documentation for our Orange 3 widget selection. Right click and select “Help” or press F1. ** ** It’s easy to use. To learn more about a particular wigdet, click on the widget. Either use right click and select “Help” or press F1. A new window will open with a widget description and an example for its use. There are also screenshots included as visual help.
By: AJDA, Aug 28, 2015
Scatter Plot Projection Rank
One of the nicest and surely most useful visualization widgets in Orange is Scatter Plot. The widget displays a 2-D plot, where x and y-axes are two attributes from the data. 2-dimensional scatter plot visualization Orange 2.7 has a wonderful functionality called VizRank, that is now implemented also in Orange 3. Rank Projections functionality enables you to find interesting attribute pairs by scoring their average classification accuracy. Click ‘Start Evaluation’ to begin ranking.
By: AJDA, Aug 14, 2015
Classifying instances with Orange in Python
Last week we showed you how to create your own data table in Python shell. Now we’re going to take you a step further and show you how to easily classify data with Orange. First we’re going to create a new data table with 10 fruits as our instances. import Orange from Orange.data import * color = DiscreteVariable("color", values=["orange", "green", "yellow"])calories = ContinuousVariable("calories") fiber = ContinuousVariable("fiber") fruit = DiscreteVariable("fruit", values=["orange", "apple", "peach"]) domain = Domain([color, calories, fiber], class_vars=fruit) data=Table(domain, [</span> ["green", 4, 1.