Learn ORANGE with Real Code Examples

Orange allows users to create data analysis workflows using a drag-and-drop GUI without coding.

It includes tools for machine learning, data visualization, bioinformatics, text mining, and add-ons for specific domains.

Orange supports scripting in Python for more advanced users and integration into custom data pipelines.

Classification and regression algorithms (tree-based, linear, ensemble)

Clustering and association analysis

Data preprocessing widgets (normalization, imputation, filtering)

Evaluation tools including cross-validation and ROC analysis

Visualization widgets for scatter plots, heatmaps, and decision trees

Widget: building block of a workflow performing a task (preprocessing, modeling, visualization)

Workflow: connected sequence of widgets representing a data analysis pipeline

Data Table: dataset representation in Orange

Add-on: optional module for domain-specific functionality

Evaluation: performance measurement of models

Workflows/ - saved .ows files

Datasets/ - CSV, Excel, or other compatible files

Python scripts/ - for automation and custom widgets

Add-ons/ - installed domain-specific extensions

Visualizations/ - exported charts and plots

Import dataset (CSV, Excel, or database)

Preprocess data using filtering, normalization, and feature selection widgets

Choose a learner widget (classifier/regressor)

Evaluate model using cross-validation or test set

Visualize results and export predictions

Beginner: simple GUI-based classification or regression

Intermediate: chain multiple widgets for preprocessing and modeling

Advanced: automate workflows using Python scripts

Expert: develop custom widgets or integrate with scikit-learn

Enterprise: combine Orange workflows with larger Python-based pipelines

Orange vs Weka: Orange Python-based with interactive GUI, Weka Java-based

Orange vs RapidMiner: Orange lightweight and interactive, RapidMiner enterprise-focused

Orange vs KNIME: Orange easier for teaching, KNIME better for large enterprise pipelines

Orange vs Python/scikit-learn: Orange visual and beginner-friendly, scikit-learn code-first

Orange vs Tableau: Orange ML-focused with some visualization, Tableau mainly for visualization

1996 – Initial development at University of Ljubljana

2004 – Orange 2.0 with GUI improvements

2010 – Orange 3 released with Python integration and add-ons

2016 – Orange 3.3 with updated widgets and visualizations

2025 – Orange 3.35+ with improved Python integration and ML add-ons

Widget: functional block in workflow

Canvas: GUI workspace for workflow building

Data Table: dataset structure in Orange

Add-on: additional functionality module

Learner: classification/regression algorithm