[Retracted] Predicting the Growth of F. proliferatum and F. culmorum and the Growth of Mycotoxin Using Machine Learning Approach

Table 2

Machine learning main parameter model and their values.

Machine learning model	Name of the parameter	Description	Standard values
Neural network	Decay	Decay rate weight	[0.2, 0.4, 0.6]
	Size	Unit of hidden layer	[5, 10, 15, 20, 25]
Random forest	ntree	Number of trees	500
	mtry	A randomly selected number of predictors	[2, 3, 5]
Extreme gradient boosted tree	Max_depth	Maximum of tree depth	[0, 1, 2, 3, 4, 5]
	Gamma	Penalty factor regularization	0
	nrounds	Number of iterations	150
	Colsample_bytree	Column fraction to be arbitrarily tested for every tree	1
	Subsample	Subsample percentage from the training established to cultivate a tree	[0.5, 0.75, 1, 1.25]
	Minimum_child_weight	Weight of low weight instance per node	0.5
	Eta	Shrinkage or rate of learning	[0.1, 0.2, 0.3]