Fig 1. Orange data science toolbox.

Orange provides data analysis components, also called widgets, assembled into a data analysis workflows through visual programming. The components typically encapsulate some data processing or modeling methods; they receive the input and submit the results to the output. Widgets in Orange are represented with icons with an input slot on the left and the output slot on the right. Users place widgets on the canvas and connect the inputs and outputs of the widgets. In this way, they define the data and information processing pipeline. The system processes the workflow on-the-fly: as soon as the widget receives the information, it would handle it and send out the results. In the workflow shown on the figure, the data pipeline starts with reading the data (File widget) and passes it to cross-validation (Test and Score), which also receives a learning algorithm on its input. Double-clicking the widget exposes its content. For instance, we pass cross-validation results to the Confusion Matrix, which shows that logistic regression misclassified only two data instances. We use the Scatter Plot to show the entire data set and also display selected data from the Confusion Matrix. Any change in selection in the Confusion Matrix would change its output. This would trigger the change in the Scatter Plot. With this composition of components, we turn this workflow into a visual explorative environment for examining cross-validation results.