Orange Data mining

Orange is an open source data visualization and analysis tool. Orange is developed at the Bioinformatics Laboratory at the Faculty of Computer and Information Science, University of Ljubljana, Slovenia, along with open source community. Data mining is done through visual programming or Python scripting.

The tool has components for machine learning, add-ons for bioinformatics and text mining and it is packed with features for data analytics. Orange is a Python library. Python scripts can run in a terminal window, integrated environments like PyCharm and PythonWin, or shells like iPython.

Orange consists of a canvas interface onto which the user places widgets and creates a data analysis workflow. Widgets offer basic functionalities such as reading the data, showing a data table, selecting features, training predictors, comparing learning algorithms, visualizing data elements, etc. The user can interactively explore visualizations or feed the selected subset into other widgets.

Orange Data mining

Orange-Visualization of interactions of genetic pathways

In Orange, data analysis process can be designed through visual programming. Orange remembers the choices, suggests most frequently used combinations. Orange has features for different visualizations, such as scatterplots, bar charts, trees, to dendrograms, networks and heatmaps. By combining the various widgets the design of data analytics framework can be done. There are over 100 widgets with coverage of most of standard data analysis tasks and specialized add-ons for Bioorange for bioinformatics.

Orange- Tree view of Orange widgets

Own widgets, can be developed and the scripting interface can be extended to create self contained add-ons, integrating with the rest of Orange, allowing components and code reuse. Orange runs on Windows, Mac OS X, and variety of Linux operating systems.

Orange-Data exploration by construction of analysis schema

Orange comes with mutliple classification and regression algorithms. New ones can be build or there are features to wrap existing learners and add some preprocessing to construct new variants.

Orange-Explorative analysis and classification trees

Orange can read files in native and other data formats. Orange is devoted to machine learning methods for classification, or supervised data mining. Classification uses two types of objects: learners and classifiers. Learners consider class-labeled data and return a classifier. Regression methods in Orange are very similar to classification. Both intended for supervised data mining, they require class-labeled data.

Learning of ensembles combines the predictions of separate models to gain in accuracy. The models may come from different training data samples, or may use different learners on the same data sets. Learners may also be diversified by changing their parameter sets. In Orange, ensembles are simply wrappers around learners. They behave just like any other learner. Given the data, they return models that can predict the outcome for any data instance.

Orange-Survey plot

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