Quasi-randomized regression model with 2 shrinkage parameters

Parameters' description can be found at https://techtonique.github.io/nnetsauce/

Ridge2Regressor(
  n_hidden_features = 5L,
  activation_name = "relu",
  a = 0.01,
  nodes_sim = "sobol",
  bias = TRUE,
  dropout = 0,
  n_clusters = 2L,
  cluster_encode = TRUE,
  type_clust = "kmeans",
  lambda1 = 0.1,
  lambda2 = 0.1,
  seed = 123L,
  backend = c("cpu", "gpu", "tpu")
)

Examples

set.seed(123)
n <- 50 ; p <- 3
X <- matrix(rnorm(n * p), n, p) # no intercept!
y <- rnorm(n)

obj <- nnetsauce::Ridge2Regressor(n_hidden_features = 5L)
print(obj$fit(X, y))
#> Ridge2Regressor(dropout=0.0)
print(obj$score(X, y))
#> [1] 0.1904074