page_title: Orange (software)
section_title: Features
hierarchical_section_title: Orange (software) / Features
caption_attribution_description: English: Classification Tree widget in Orange 3.0
context_page_description: Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for explorative data analysis and interactive data visualization.
context_section_description: 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.
  Canvas: graphical front-end for data analysis
  Widgets:
  Data: widgets for data input, data filtering, sampling, imputation, feature manipulation and feature selection
  Visualize: widgets for common visualization (box plot, histograms, scatter plot) and multivariate visualization (mosaic display, sieve diagram).
  Classify: a set of supervised machine learning algorithms for classification
  Regression: a set of supervised machine learning algorithms for regression
  Evaluate: cross-validation, sampling-based procedures, reliability estimation and scoring of prediction methods
  Unsupervised: unsupervised learning algorithms for clustering (k-means, hierarchical clustering) and data projection techniques (multidimensional scaling, principal component analysis, correspondence analysis).
  Add-ons:
  Associate: widgets for mining frequent itemsets and association rule learning
  Bioinformatics: widgets for gene set analysis, enrichment, and access to pathway libraries
  Data fusion: widgets for fusing different data sets, collective matrix factorization, and exploration of latent factors
  Educational: widgets for teaching machine learning concepts, such as k-means clustering, polynomial regression, stochastic gradient descent, ...
  Geo: widgets for working with geospatial data
  Image analytics: widgets for working with images and ImageNet embeddings
  Network: widgets for graph and network analysis
  Text mining: widgets for natural language processing and text mining
  Time series: widgets for time series analysis and modeling
  Spectroscopy: widgets for analyzing and visualization of (hyper)spectral datasets
caption: Classification Tree widget in Orange 3.0
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