Orange Data Mining Library

Tutorial

This is a gentle introduction on scripting in Orange , a Python 3 data mining library. We here assume you have already downloaded and installed Orange from its github repository and have a working version of Python. In the command line or any Python environment, try to import Orange. Below, we used a Python shell:

% python
>>> import Orange
>>> Orange.version.version
'3.25.0.dev0+3bdef92'
>>>

If this leaves no error and warning, Orange and Python are properly installed and you are ready to continue with the tutorial.

• The Data
  • Data Input
  • Creating a Data Table
  • Saving the Data
  • Exploration of the Data Domain
  • Data Instances
  • Orange Datasets and NumPy
  • Meta Attributes
  • Missing Values
  • Data Selection and Sampling
• Classification
  • Learners and Classifiers
  • Probabilistic Classification
  • Cross-Validation
  • Handful of Classifiers
• Regression
  • Handful of Regressors
  • Cross Validation

Reference

Available classes and methods.

• Data model (data)
  • Data Storage (storage)
  • Data Table (table)
  • SQL table (data.sql)
  • Domain description (domain)
  • Variable Descriptors (variable)
  • Values (value)
  • Data Instance (instance)
  • Data Filters (filter)
  • Loading and saving data (io)
• Data Preprocessing (preprocess)
  • Impute
  • Discretization
  • Continuization
  • Normalization
  • Randomization
  • Remove
  • Feature selection
  • Preprocessors
• Outlier detection (classification)
  • One Class Support Vector Machines
  • Elliptic Envelope
  • Local Outlier Factor
  • Isolation Forest
• Classification (classification)
  • Logistic Regression
  • Random Forest
  • Simple Random Forest
  • Softmax Regression
  • k-Nearest Neighbors
  • Naive Bayes
  • Support Vector Machines
  • Linear Support Vector Machines
  • Nu-Support Vector Machines
  • Classification Tree
  • Simple Tree
  • Majority Classifier
  • Neural Network
  • CN2 Rule Induction
  • Calibration and threshold optimization
  • Gradient Boosted Trees
• Regression (regression)
  • Linear Regression
  • Polynomial
  • Mean
  • Random Forest
  • Simple Random Forest
  • Regression Tree
  • Neural Network
  • Gradient Boosted Trees
  • Curve Fit
• Clustering (clustering)
  • Hierarchical (hierarchical)
• Distance (distance)
  • Distance
  • Handling discrete and missing data
  • Supported distances
• Evaluation (evaluation)
  • Sampling procedures for testing models (testing)
  • Scoring methods (scoring)
  • Performance curves
• Projection (projection)
  • PCA
  • FreeViz
  • LDA
  • References
• Miscellaneous (misc)
  • Distance Matrix (distmatrix)