Research Article
[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] |
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