Learn ORANGE with Real Code Examples

Load Iris dataset using File widget

Preprocess data with Normalize and Select Columns widgets

Train Random Forest classifier

Evaluate performance with Test & Score widget

Visualize feature importance and confusion matrix

Ensure dataset format is compatible

Check Python dependencies if using scripting or add-ons

Verify widget connections in workflow

Monitor memory usage for large datasets

Update add-ons and Orange version to fix compatibility issues

Validate workflows with test datasets

Compare multiple learners using Test & Score

Check preprocessing impact with separate widgets

Monitor runtime for complex workflows

Test exported models in Python or CSV outputs

Use Python scripts for programmatic execution

Export trained models for use in Python pipelines

Share .ows workflow files with colleagues

Integrate Orange widgets into Jupyter Notebooks

Visualize and export results for reporting

Python scripting for advanced analysis

Add-ons for bioinformatics, text mining, network analysis

Integration with scikit-learn, NumPy, SciPy, pandas

Visualization widgets for exploratory data analysis

Orange Canvas for GUI workflow building

Python scripts and libraries

CSV, Excel, SQL, and Pandas DataFrames

scikit-learn models and pipelines

Jupyter Notebooks for interactive analysis

Add-ons for specialized data types and visualizations

Leverage GUI Canvas for rapid experimentation

Use Python scripting for batch or repetitive tasks

Install add-ons for specialized needs

Keep workflows simple and modular

Visualize results to catch errors early

Handling larger datasets

Mastering widget chaining for complex workflows

Customizing workflows using Python

Integrating with other Python-based ML pipelines

Reproducibility across different Orange versions