Min objective and function evaluations
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As I was learning to optimize regression tree, I'm struggling to understand some of the codes and graphs generated in the matlab example ' Optimize Regression Tree'
load carsmall
X = [Weight,Horsepower];
Y = MPG;
rng default
Mdl = fitrtree(X,Y,'OptimizeHyperparameters','auto',...
'HyperparameterOptimizationOptions',struct('AcquisitionFunctionName',...
'expected-improvement-plus'))
As you can see from the above code, they set the 'OptimizeHyperparameters' to 'auto', they struct 'AcquisitionFunctionName' to 'expected-improvement-plus', they also put 'HyperparameterOptimizationOptions' in the bracket.
My first question is that i'm not familiar with all the parameters I could put here, is there a list of those parameters out there for me to familiarize with all the properties I could put in the bracket?
Once you type the above code, the outputs are two graphs shown below.
![OptimizeARegressionTreeExample_01.png](https://www.mathworks.com/matlabcentral/answers/uploaded_files/200773/OptimizeARegressionTreeExample_01.png)
![OptimizeARegressionTreeExample_02.png](https://www.mathworks.com/matlabcentral/answers/uploaded_files/200774/OptimizeARegressionTreeExample_02.png)
My second question is that in the first graph, what does 'Min objective' mean? What does 'Number of function Evaluations' mean?
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Don Mathis
il 16 Gen 2019
Question 1: https://www.mathworks.com/help/stats/fitrtree.html#bt6cr84_sep_shared-HyperparameterOptimizationOptions
Question 2: As mentioned in the link for Question 1, it's using the 'bayesopt' function. Start here: https://www.mathworks.com/help/stats/bayesopt.html
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