Unified S3 Interface to Machine Learning Models

Provides a unified and consistent S3 interface for training and predicting with a variety of machine learning models in R. The package wraps popular algorithms (e.g., from 'glmnet', 'lightgbm', 'ranger', 'e1071', and 'caret') under a common workflow based on simple wrap_*() and predict() functions, allowing users to switch between models without changing their code structure. It supports both classification and regression tasks and facilitates rapid experimentation, benchmarking, and comparison of models. By abstracting away package-specific APIs while preserving flexibility in parameter specification, the package streamlines machine learning workflows and promotes reproducibility.

mlS3

R package mlS3 providing an S3 interface to Machine Learning packages.

Install

• From GitHub for the development version

remotes::install_github("Techtonique/mlS3")

• From CRAN

install.packages("mlS3")

Classification example

library(mlS3)

# =============================================================================
# Classification examples (no leakage)
# =============================================================================
set.seed(123)

# --- Binary classification: iris setosa vs versicolor ---
iris_bin <- iris[iris$Species != "virginica", ]
X_bin <- iris_bin[, 1:4]
y_bin <- droplevels(iris_bin$Species)

# Split into train/test
idx_bin <- sample(nrow(X_bin), 0.7 * nrow(X_bin))
X_bin_train <- X_bin[idx_bin, ]
y_bin_train <- y_bin[idx_bin]
X_bin_test  <- X_bin[-idx_bin, ]
y_bin_test  <- y_bin[-idx_bin]

# glmnet
mod <- wrap_glmnet(X_bin_train, y_bin_train, family = "binomial")
pred_bin_glmnet <- predict(mod, newx = X_bin_test, type = "class")
acc_glmnet <- mean(pred_bin_glmnet == y_bin_test)

cat("Accuracy (glmnet): ", acc_glmnet, "\n")


# --- Multiclass classification: iris all species ---
X_multi <- iris[, 1:4]
y_multi <- iris$Species

# Split into train/test
idx_multi <- sample(nrow(X_multi), 0.7 * nrow(X_multi))
X_multi_train <- X_multi[idx_multi, ]
y_multi_train <- y_multi[idx_multi]
X_multi_test  <- X_multi[-idx_multi, ]
y_multi_test  <- y_multi[-idx_multi]

# lightgbm
mod <- wrap_lightgbm(X_multi_train, y_multi_train,
                     params = list(objective = "multiclass",
                                   num_class = 3, verbose = -1),
                     nrounds = 150)
pred_multi_lightgbm <- predict(mod, newx = X_multi_test, type = "class")
acc_lightgbm <- mean(pred_multi_lightgbm == y_multi_test)

# ranger
mod <- wrap_ranger(X_multi_train, y_multi_train, num.trees = 100L)
pred_multi_ranger <- predict(mod, newx = X_multi_test, type = "class")
acc_ranger <- mean(pred_multi_ranger == y_multi_test)

# svm
mod <- wrap_svm(X_multi_train, y_multi_train, kernel = "radial")
pred_multi_svm <- predict(mod, newx = X_multi_test, type = "class")
acc_svm <- mean(pred_multi_svm == y_multi_test)

cat("Accuracy (lightgbm): ", acc_lightgbm, "\n")
cat("Accuracy (ranger): ", acc_ranger, "\n")
cat("Accuracy (svm): ", acc_svm, "\n")

Regression example

# =============================================================================
# Regression examples (mtcars)
# =============================================================================
X_reg <- mtcars[, -1]
y_reg <- mtcars$mpg

# Split into train/test
set.seed(123)
idx_reg <- sample(nrow(X_reg), 0.7 * nrow(X_reg))
X_reg_train <- X_reg[idx_reg, ];  y_reg_train <- y_reg[idx_reg]
X_reg_test  <- X_reg[-idx_reg, ]; y_reg_test  <- y_reg[-idx_reg]

# lightgbm
mod <- wrap_lightgbm(X_reg_train, y_reg_train,
                     params = list(objective = "regression", verbose = -1),
                     nrounds = 50)
pred_reg_lightgbm <- predict(mod, newx = X_reg_test)
rmse_lightgbm <- sqrt(mean((pred_reg_lightgbm - y_reg_test)^2))

# glmnet
mod <- wrap_glmnet(X_reg_train, y_reg_train, alpha = 0)
pred_reg_glmnet <- predict(mod, newx = X_reg_test)
rmse_glmnet <- sqrt(mean((pred_reg_glmnet - y_reg_test)^2))

# svm
mod <- wrap_svm(X_reg_train, y_reg_train)
pred_reg_svm <- predict(mod, newx = X_reg_test)
rmse_svm <- sqrt(mean((pred_reg_svm - y_reg_test)^2))

# ranger
mod <- wrap_ranger(X_reg_train, y_reg_train, num.trees = 100L)
pred_reg_ranger <- predict(mod, newx = X_reg_test)
rmse_ranger <- sqrt(mean((pred_reg_ranger - y_reg_test)^2))

cat("RMSE (lightgbm): ", rmse_lightgbm, "\n")
cat("RMSE (glmnet): ", rmse_glmnet, "\n")
cat("RMSE (svm): ", rmse_svm, "\n")
cat("RMSE (ranger): ", rmse_ranger, "\n")