Orange Blog
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, Oct 2, 2015
A visit from the Tilburg University
Biolab is currently hosting two amazing data scientists from the Tilburg University - dr. Marie Nilsen and dr. Eric Postma, who are preparing a 20-lecture MOOC on data science for non-technical audience. A part of the course will use Orange. The majority of their students is coming from humanities, law, economy and behavioral studies, thus we are discussing options and opportunities for adapting Orange for social scientists. Another great thing is that the course is designed for beginner level data miners, showcasing that anybody can mine the data and learn from it.
By: AJDA, Sep 25, 2015
Save your graphs!
If you are often working with Orange, you probably have noticed a small button at the bottom of most visualization widgets. “Save Graph” now enables you to export graphs, charts, and hierarchical trees to your computer and use them in your reports. Because people need to see it to believe it! “Save Graph” will save visualizations to your computer. Save Graph function is available in Paint Data, Image Viewer, all visualization widgets, and a few others (list is below).
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.
By: AJDA, Aug 7, 2015
Creating a new data table in Orange through Python
IMPORT DATA One of the first tasks in Orange data analysis is of course loading your data. If you are using Orange through Python, this is as easy as riding a bike: import Orange data = Orange.data.Table(“iris”) print (data) This will return a neat data table of the famous Iris data set in the console. CREATE YOUR OWN DATA TABLE What if you want to create your own data table from scratch?
By: AJDA, Jul 31, 2015
Datasets in Orange Bioinformatics Add-On
As you might know, Orange comes with several basic widget sets pre-installed. These allow you to upload and explore the data, visualize them, learn from them and make predictions. However, there are also some exciting add-ons available for installation. One of these is a bioinformatics add-on, which is our specialty. Bioinformatics widget set allows you to pursue complex analysis of gene expression by providing access to several external libraries. There are four widgets intended specifically for this - dictyExpress, GEO Data Sets, PIPAx and GenExpress.
By: AJDA, Jul 24, 2015
Visualizing Misclassifications
In data mining classification is one of the key methods for making predictions and gaining important information from our data. We would, for example, use classification for predicting which patients are likely to have the disease based on a given set of symptoms. In Orange an easy way to classify your data is to select several classification widgets (e.g. Naive Bayes, Classification Tree and Linear Regression), compare the prediction quality of each learner with Test Learners and Confusion Matrix and then use the best performing classifier on a new data set for classification.