Learn ORANGE with Real Code Examples

// In Orange Canvas:
// 1. Add 'File' widget to load dataset
// 2. Add 'Data Table' or 'Scatter Plot' for visualization
// 3. Add 'Test & Score' or 'Classification Tree' for modeling
// 4. Connect widgets to build the workflow interactively
// Python scripting can extend the workflow for advanced tasks.

An example showing a simple workflow in Orange for classification or clustering using the visual interface.

// In Orange Canvas:
// 1. Load dataset using 'File' widget
// 2. Add 'Select Columns' to choose features and target
// 3. Add 'Random Forest' or 'Logistic Regression' widget
// 4. Connect to 'Test & Score' to evaluate
// 5. Optionally add 'Confusion Matrix' to view results

A visual workflow for performing classification using Orange widgets.

// In Orange Canvas:
// 1. Load dataset
// 2. Select features and numeric target
// 3. Add 'Linear Regression' or 'Random Forest Regression'
// 4. Connect to 'Test & Score' for evaluation
// 5. Visualize predictions with 'Scatter Plot' widget

Build a regression workflow using Orange visual programming.

// In Orange Canvas:
// 1. Load dataset
// 2. Add 'Select Columns' if needed
// 3. Add 'K-Means' or 'Hierarchical Clustering'
// 4. Connect to 'Silhouette Plot' or 'Data Table' to inspect clusters

Clustering workflow using Orange widgets like K-Means or Hierarchical Clustering.

// In Orange Canvas:
// 1. Load dataset
// 2. Add 'Impute' widget to fill missing values
// 3. Add 'Normalize' or 'Continuize' if needed
// 4. Connect to modeling widgets for training

Preprocess data using Orange widgets like normalization and missing value imputation.

// In Orange Canvas:
// 1. Load dataset
// 2. Add 'Rank' widget to evaluate feature importance
// 3. Add 'Select Columns' to choose top features
// 4. Connect to modeling and evaluation widgets

Selecting important features using Orange's Rank or Select Columns widgets.

// In Orange Canvas:
// 1. Load text data using 'File' or 'Corpus'
// 2. Add 'Preprocess Text' for tokenization and stopword removal
// 3. Convert text to vectors with 'Bag of Words' or 'TF-IDF'
// 4. Connect to 'Naive Bayes' or 'Logistic Regression'
// 5. Evaluate using 'Test & Score'

Process text data in Orange using prebuilt text mining widgets.

// In Orange Canvas:
// 1. Load dataset
// 2. Add classifier widgets
// 3. Connect to 'Test & Score' for cross-validation
// 4. Optionally add 'Confusion Matrix', 'ROC Analysis', or 'Predictions'

Evaluating models using Orange's Test & Score and visualization widgets.

// In Orange Canvas:
// 1. Load dataset
// 2. Add 'Random Forest' or 'AdaBoost'
// 3. Connect to 'Test & Score' for evaluation
// 4. Visualize results with 'Confusion Matrix' or 'Scatter Plot'

Using ensemble methods like Random Forest or AdaBoost in Orange Canvas.

// In Orange Canvas:
// 1. Add 'Python Script' widget
// 2. Import data from previous widgets
// 3. Write custom Python code to manipulate or visualize data
// 4. Output processed data to next widget
// Example:
// data = in_data
// predictions = model(data)
// out_data = predictions

Extending Orange workflows using Python scripting for custom analysis.