add elastic net algorithm

NEWS.md

@@ -1,5 +1,6 @@
 # splendid 0.4.0
 
+* add elastic net penalty for multinomial logistic regression
 * fix calculation of multi-class log loss metric
 * increased minimum R version to 4.1.0
 * fix warnings pertaining to deprecated functions

R/classification.R

@@ -57,6 +57,7 @@ classification <- function(data, class, algorithms, rfe = FALSE, ova = FALSE,
     mlr_glm = mlr_model(data, class, "mlr_glm"),
     mlr_lasso = cv_mlr_model(data, class, "mlr_lasso", seed_alg),
     mlr_ridge = cv_mlr_model(data, class, "mlr_ridge", seed_alg),
+    mlr_enet = enet_model(data, class, seed_alg),
     mlr_nnet = mlr_model(data, class, "mlr_nnet"),
     nnet = nnet_model(data, class),
     nbayes = nbayes_model(data, class),
@@ -307,6 +308,28 @@ cv_mlr_model <- function(data, class, algorithms, seed_alg = NULL) {
   }
 }
 
+#' Elastic net model
+#' @noRd
+enet_model <- function(data, class, seed_alg = NULL) {
+  if (!is.null(seed_alg)) set.seed(seed_alg)
+  if (!requireNamespace("caret", quietly = TRUE)) {
+    stop("Package \"caret\" is needed. Please install it.",
+         call. = FALSE)
+  } else {
+    caret::train(
+      x = as.matrix(data),
+      y = class,
+      method = "glmnet",
+      tuneLength = 10,
+      trControl = caret::trainControl(
+        method = "cv",
+        number = 5,
+        classProbs = TRUE
+      )
+    )
+  }
+}
+
 #' neural network model
 #' @noRd
 nnet_model <- function(data, class) {

R/splendid.R

@@ -24,6 +24,7 @@
 #'     * Generalized Linear Model with no penalization ("mlr_glm")
 #'     * GLM with LASSO penalty ("mlr_lasso")
 #'     * GLM with ridge penalty ("mlr_ridge")
+#'     * GLM with elastic net penalty ("mlr_enet")
 #'     * Neural Networks ("mlr_nnet")
 #' * Neural Networks ("nnet")
 #' * Naive Bayes ("nbayes")

R/utils.R

@@ -3,14 +3,14 @@ globalVariables(".")
 
 # Algorithm functions and classes
 ALG.NAME <- c("pam", "svm", "rf", "lda", "slda", "sdda", "mlr_glm", "mlr_lasso",
-              "mlr_ridge", "mlr_nnet", "nnet", "nbayes", "adaboost",
+              "mlr_ridge", "mlr_enet", "mlr_nnet", "nnet", "nbayes", "adaboost",
               "adaboost_m1", "xgboost", "knn")
 ALG.CLASS <- c("pamrtrained", "train", "svm", "randomForest", "lda", "sda",
                "cv.glmnet", "glmnet", "multinom", "nnet.formula", "naiveBayes",
                "maboost", "boosting", "xgb.Booster", "knn")
 
 # Algorithms that need all continuous predictors
-ALG.CONT <- c("svm", "lda", "mlr_glm", "mlr_lasso", "mlr_ridge")
+ALG.CONT <- c("svm", "lda", "mlr_glm", "mlr_lasso", "mlr_ridge", "mlr_enet")
 
 #' Redirect any console printouts from print() or cat() to null device
 #' @references