Which cross-validation method is used in Hyperparameters optimisation for fitrnet?

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Phan Quoc Khang Nguyen
编辑： Daksh 2022-12-21
Train neural network regression model - MATLAB fitrnet - MathWorks Australia
In the example "Minimize Cross-Validation Error in Neural Network" (above link), the code is stated to reduce the cross-validation loss of over some problem hyperparameters by using Bayesian optimization. However, something is not clear, can somebody help with the followings?
  1. The data was splited into 80% train and 20% test. So the 20% test set is obviously not for cross-validation?
  2. If so, is it true that the cross-validation loss is calculated internally using train set? And which type of cross-validation method is it? (Kfold, holdout, leaveout,...?
  3. For the optimisation process, the software first starts with a set of parametes, train that network and calculate the value of ln(1+loss) and then modify the paramters to minimise it. If I am wrong or lacking something, can you help me explain the whole optimising procedure?
Sorry if these questions are too trivial as I am only a beginner in using these features.

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回答（1 个）

Daksh
Daksh 2022-12-21
编辑：Daksh 2022-12-21
It is my understanding that you wish to understand more about the Cross validation method used in hyperparameter optimization for Fitrnet. Refer to these points for answers to your queries:
  • The training set (80%) is used for cross validation and training the model, while the test set is only used to assess how the model performs when faced with new data.
c = cvpartition(height(cars),"Holdout",0.20);
  • The above link provided by you indicated that "Holdout" method for cross validation is used.
  • Refer the following link for Bayesian Hyperparameter Optimization, it should help you understand better the concept of optimization and model training with code, diagrams and visualizations.
Hope it helps

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