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Min objective and function evaluations

Asked by Salad Box on 15 Jan 2019
Latest activity Answered by Don Mathis on 16 Jan 2019
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
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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1 Answer

Answer by Don Mathis on 16 Jan 2019

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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