nnetsauce

Randomized and quasi-randomized nnetworks for supervised learning and multivariate time series forecasting

This is the R version of Python’s Techtonique/nnetsauce. See these posts for more details.

If you encounter errors on Windows, envisage using the Windows Subsystems for Linux.

Install

From GitHub

remotes::install_github("Techtonique/nnetsauce_r")

From R-universe

install.packages('nnetsauce', repos = c('https://techtonique.r-universe.dev',
'https://cloud.r-project.org'))

Examples

Keep in mind that there are many other models implemented. See these posts.

Classification

library(datasets)
#'
set.seed(123)
X <- as.matrix(iris[, 1:4])
y <- as.integer(iris$Species) - 1L
#'
(index_train <- base::sample.int(n = nrow(X),
                                 size = floor(0.8*nrow(X)),
                                 replace = FALSE))
X_train <- X[index_train, ]
y_train <- y[index_train]
X_test <- X[-index_train, ]
y_test <- y[-index_train]
#'
obj <- LazyClassifier()
res <- obj$fit(X_train, X_test, y_train, y_test)
print(res[[1]])

Regression

X <- MASS::Boston[,-14] # dataset has an ethical problem
y <- MASS::Boston$medv

set.seed(13)
(index_train <- base::sample.int(n = nrow(X),
                                 size = floor(0.8*nrow(X)),
                                 replace = FALSE))
X_train <- X[index_train, ]
y_train <- y[index_train]
X_test <- X[-index_train, ]
y_test <- y[-index_train]

obj <- LazyRegressor()
res <- obj$fit(X_train, X_test, y_train, y_test)
print(res[[1]])

Time series

set.seed(123)
X <- matrix(rnorm(300), 100, 3)

(index_train <- base::sample.int(n = nrow(X),
                                 size = floor(0.8*nrow(X)),
                                 replace = FALSE))
X_train <- data.frame(X[index_train, ])
X_test <- data.frame(X[-index_train, ])

obj <- LazyMTS()

res <- obj$fit(X_train, X_test)
print(res[[1]])