nn_domain_score {viraldomain} | R Documentation |
Calculate the Neural Network model domain applicability score
Description
This function fits a Neural Network model to the provided data and computes a domain applicability score based on PCA distances.
Usage
nn_domain_score(
featured_col,
train_data,
nn_hyperparameters,
test_data,
threshold_value
)
Arguments
featured_col |
The name of the featured column in the training data. |
train_data |
The training data used to fit the Neural Network model. |
nn_hyperparameters |
A list of Neural Network hyperparameters, including hidden_units, penalty, and epochs. |
test_data |
The testing domain data used to calculate the domain applicability score. |
threshold_value |
The threshold value for domain applicability scoring. |
Value
A tibble with the domain applicability scores.
Examples
set.seed(123)
library(dplyr)
featured_col <- "cd_2022"
# Specifying features for training and testing procedures
train_data = viral %>%
dplyr::select(cd_2022, vl_2022)
test_data = sero
nn_hyperparameters <- list(hidden_units = 1, penalty = 0.3746312, epochs = 480)
threshold_value <- 0.99
nn_domain_score(featured_col, train_data, nn_hyperparameters, test_data, threshold_value)
[Package viraldomain version 0.0.7 Index]