Boosted Configuration (Neural) Networks
From Github:
devtools::install_github("Techtonique/bcn")
# Browse the bcn manual pages
help(package = 'bcn')From R universe:
# Enable repository from techtonique
options(repos = c(
techtonique = 'https://techtonique.r-universe.dev',
CRAN = 'https://cloud.r-project.org'))
# Download and install bcn in R
install.packages('bcn')
# Browse the bcn manual pages
help(package = 'bcn')
# split data into training/test sets
set.seed(1234)
train_idx <- sample(nrow(iris), 0.8 * nrow(iris))
X_train <- as.matrix(iris[train_idx, -ncol(iris)])
X_test <- as.matrix(iris[-train_idx, -ncol(iris)])
y_train <- iris$Species[train_idx]
y_test <- iris$Species[-train_idx]
# adjust bcn to training set
fit_obj <- bcn::bcn(x = X_train, y = y_train, B = 10, nu = 0.335855,
lam = 10**0.7837525, r = 1 - 10**(-5.470031), tol = 10**-7,
activation = "tanh", type_optim = "nlminb")
# accuracy
print(mean(predict(fit_obj, newx = X_test) == y_test))
library(MASS)
data("Boston") # dataset has an ethical problem
set.seed(1234)
train_idx <- sample(nrow(Boston), 0.8 * nrow(Boston))
X_train <- as.matrix(Boston[train_idx, -ncol(Boston)])
X_test <- as.matrix(Boston[-train_idx, -ncol(Boston)])
y_train <- Boston$medv[train_idx]
y_test <- Boston$medv[-train_idx]
fit_obj <- bcn::bcn(x = X_train, y = y_train, B = 500, nu = 0.5646811,
lam = 10**0.5106108, r = 1 - 10**(-7), tol = 10**-7,
col_sample = 0.5, activation = "tanh", type_optim = "nlminb")
print(sqrt(mean((predict(fit_obj, newx = X_test) - y_test)**2)))