I have a very simple MATLAB program for training and testing a regression tree, I use the same Data carsmall that is in the tutorial examples:
clear all
clc
close all
load carsmall
X = [Cylinders, Weight, Horsepower, Displacement];
Y = MPG;
tree = fitrtree(X,Y, 'PredictorNames',{'Cylinders', 'Weight', 'Horsepower', 'Displacement'},'ResponseName','MPG','MinLeaf',10);
Xtest=[6,4100,150,130];
MPGest = predict(tree, Xtest);
This gives as a result MPGest=14.9167
I want to know how the predict function is arriving at that value, usually to understand I go line by line inside the function. This one is very tricky because uses classes so I arrive at this line
node = findNode(this.Impl,X,this.DataSummary.CategoricalPredictors,subtrees);
and inside that function I arrive to
n = classreg.learning.treeutils.findNode(X,...
subtrees,this.PruneList,...
this.Children',iscat,...
this.CutVar,this.CutPoint,this.CutCategories,...
this.SurrCutFlip,this.SurrCutVar,...
this.SurrCutPoint,this.SurrCutCategories,...
verbose);
when I try to step in at this step it just give me n=10, how is MATLAB arriving at this number? for example, If I wanted to make my own program to calculate this number using the tree object as input without using predict?
