Imneuron {Imneuron}

Fitting of AI based Neural Network Model

Description

Fitting of AI based Neural Network Model

Usage

Imneuron(data, target_variable, hidden_neurons_range, cv_type = "5-fold")

Arguments

data

dataset containing the information about all the variables which are continuous in nature

target_variable

response variable

hidden_neurons_range

This is a range of values specifying the number of hidden neurons to explore in the neural network's two layers (Layer 1 and Layer 2)

cv_type

This argument is used to apply cross validation like "5_fold" for 5 folded cross validation and "10-fold" for 10 folded cross validation

Value

Average values of R2, RMSE, MAE, and PER across the cross-validation folds. The trained neural network models for each fold. A data frame containing the evaluation metrics for each fold

References

Jeelani, M.I., Tabassum, A., Rather, K and Gul, M. (2023). Neural Network Modeling of Height Diameter Relationships for Himalayan Pine through Back Propagation Approach. Journal of The Indian Society of Agricultural Statistics. 76(3): 169. Tabassum, A., Jeelani, M.I., Sharma,M., Rather, K R ., Rashid, I and Gul, M. (2022). Predictive Modelling of Height and Diameter Relationships of Himalayan Chir Pine. Agricultural Science Digest - A Research Journal. <doi:10.18805/ag.D-5555>

Examples

# 5-fold cross-validation

data(fruit)
results_5fold <- Imneuron(fruit, "Fruit.Yield", hidden_neurons_range = c(2,2), cv_type = "5-fold")
results_10fold <- Imneuron(fruit, "Fruit.Yield", hidden_neurons_range = c(2,2), cv_type = "10-fold")