https://github.com/juliaai/treerecipe.jl

Plot recipe for plotting (decision) trees
https://github.com/juliaai/treerecipe.jl

Last synced: 7 days ago
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Plot recipe for plotting (decision) trees

• Host: GitHub
• URL: https://github.com/juliaai/treerecipe.jl
• Owner: JuliaAI
• License: mit
• Created: 2022-11-15T14:08:50.000Z (almost 2 years ago)
• Default Branch: master
• Last Pushed: 2023-11-02T16:45:31.000Z (about 1 year ago)
• Last Synced: 2024-07-10T02:21:28.583Z (4 months ago)
• Language: Julia
• Size: 29.3 KB
• Stars: 13
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# TreeRecipe.jl

A Plot recipe for plotting (decision) trees.

A plot recipe (based on `RecipeBase.jl`) to create a graphical representation of a tree.

The recipe has originally been designed to plot decision trees, but it is able to plot all sort

of trees which conform to the following rules:

- The tree must be wrapped in an `AbstractTrees`-interface. I.e. it has

  - to be a subtype of `AbstractTrees.AbstractNode{T}`

  - implement `AbstractTrees.children()`

  - implement `AbstractTrees.printnode()`

See [`DecisionTree.jl/abstract_trees.jl`](https://github.com/JuliaAI/DecisionTree.jl/blob/9dab9c12fcf2d54d4591b23fc87512964fb664b8/src/abstract_trees.jl) for 

an exemplary implementation of the concept. In addition there are examples in the `examples`-folder which show how the recipe can be applied to plot decision trees from the `DecisionTree.jl`-package as well as from the `BetaML.jl`-package.

This approach taken by `TreeRecipe` ensures that a tree implementation can be plotted without having any dependencies to a graphics package and it ensures furthermore, that the recipe is independent of the implementation details of the tree. 

For more information have a look at the article 

["If things are not ready to use"](https://towardsdatascience.com/part-iii-if-things-are-not-ready-to-use-59d2db378bec) in *Towards Data Science* where the basic ideas are explained.

And here you get an impression on how a plot of a decision tree might look like (in this case a `DecsionTree` with the Iris dataset):