# Using a decision tree with 3 dimensional input points

I implemented a normal classification tree (that uses the Gini index to look for a split). I am using it to predict the age of people. My input data was a series of points on 1 axis (Only X coordinate), and output was an age (9 years old, 10 years old....).

Example:

p1 = 1.3, p2 = 3.4, p3 = 2.1 ........ => Output = 9 years old

p1 = 1.4, p2 = 2.4, p3 = 2.6 ........ => Output = 10 years old

My tree looks something like (just an example):

if p1 > 1
if p2 < 3
output = 10 years old
else
output = 9 years old
else
...


This is working fine.

Now I want to modify it, to work with 3D points. I mean points that have X, Y, Z and not just X.

So my input data would be like:

p1 = [1.2, 1.5, 4.3], p2 = [4.2, 1.3, 5.2] .... => output = 9 years old

How do I approach this? should my tree use each coordinate as a seperate input (p1x, p1y, p1z, p2x, p2y....) or is there an approach that can be taken for my case?

Note: I am using classification trees but if you think there's another way of predicting in my case, I would love to know about it.

Also I'm using python code, but I'm looking for the logic not the programming part.

Thanks