Wow! 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
  

  https://codap.xyz/plugins/arbor/

• 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.