Optimization of Tree-Based Machine Learning Models to Predict the Length of Hospital Stay Using Genetic Algorithm
Table 6
Mechanism of tree-based HPO with GA.
Input: Number of folds for cross-validationK, dataset D, number of generations , population size , crossover probability CP, and mutation probability MP
Output: the optimum value of tree-based models hyperparameter
= 0
Initialize population randomly of size
While < G do:
= + 1
For p = 1 to do:
Use the GA solutions for tree-based hyperparameters from the pth individual
For k = 1 to K do:
Divide D into K parts, 1 part as the testing set Test-S and () parts as the
training set Train-S
Train the tree-based model on the training set Train-S
Predict the test set Test-S using the trained tree-based model
Calculate the mean square errors (MSEs)
End for
Calculate the value of the fitness function based on calculated MSEs
End for
Select 2 individuals by the steady-state selection method
Use the uniform crossover operator with the probability CP on selected individuals
Use the mutation operator with the probability MP on a new individual
Add the new individual to the population
End while
Return the optimum hyperparameter values of tree-based models