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Koki Hosoda
ParticipantIn the example of CODAP activities, there is an activity on decision trees. Could you tell me how to create a decision tree model with my original data? I want to take my data and make a decision tree model freely.
March 3, 2022 at 7:17 pm #6899Tim Erickson
ParticipantWow! The first person to ask about this! I’m not sure where you’re starting, so I apologize if this advice is too basic or too advanced. Please keep replying if you need more help, or contact me directly.
One thing to try is to play a little game I wrote. Here is the link.
But you may want more instructions about how to set it up:
The decision tree tool, named Arbor, is a plugin. So the basic idea is:
- Make a CODAP document with your data
- Put the plugin into your document
- Do your analysis.
But how do you put the plugin in your document? There are several ways. Here is one:
- Choose Import from the “hamburger” menu in CODAP’s upper left.
- In the URL option, enter
- Click Import
You should see an empty tool with the label “drop your target attribute here.”
Drag the attribute you’re trying to predict from your data table and drop it in that area. You’ll see the beginning of a tree, describing the target attribute, which direction is “positive,” and the base rate.
To make a branch, drag a new attribute onto the bottom (white) box, the one with the base rate. The tree will branch according to values of that attribute.
Click the circular gray areas with (?) to assign diagnoses (predictions) to each branch. You will see the numbers of false positives (FP), etc., update at the bottom of the tree.
Various tabs let you control options and see a confusion matrix. There is also a help panel with more details.
To analyze the effectiveness of your tree and compare it to other trees you have made (suppose you want to make something like an AUC-ROC curve…) you need to “emit” data using the controls near the bottom of the Arbor tool. But note that this tool does not do logistic regression! You’re just making trees by hand!
I would be interested to know how many of our readers here would like, for example, a paper or a video describing more of this.
July 1, 2025 at 12:18 pm #12257hudakoulani
ParticipantHello Tim,
I am currently trying to get to know the plugin that are developed for CODAP, especially those that provide functionalities for machine learning, such as decision trees.
I have looked into the list on this page (https://concord-consortium.github.io/codap-data-interactives/build/) but unfortunately I couldnt find the Arbor Plugin for the decision tree you mentioned in your answer. I have copied your link and it worked, but I am asking to find out if there are other interesting plugin that are not mentioned in the list but do exist 🙂 I would be perfect if you could provide some information about that. I came across the GitHub repo (https://github.com/concord-consortium/codap-data-interactives) that hosts the code for the plugins, but the Arbor plugin is not to be found there either and thus my question.
Thank you very much!
Huda from Germany
July 2, 2025 at 12:28 am #12264Tim Erickson
ParticipantHi Huda!
I post my plugins at https://codap.xyz. You will find a number of things there that might be of interest. Some of them eventually get adopted by the CODAP gurus (like Choosy and Testimate). Others, like Arbor, the tree plugin, do not appear in the CODAP Plugins menu or in their GIT repository.
In the case of Arbor, you can find it in my own repository (https://github.com/eepsmedia/plugins/tree/master/arbor), but for right now, beware! I’m having trouble communicating with github, so the very latest changes have not been merged to that repo. If you need the latest code, let me know and I’ll just send it.
Best yet, convince Yannik Fleischer or Sven Hüsing at Paderborn to take over this code…
Schöne Grüße,
Tim
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