Overview

Following the R6 object-based package unifiedml introduced last week, this blog post introduces the mlS3 R package, which strives to provide a unified, consistent S3 interface for training and predicting from a variety of popular machine learning models. Rather than learning the idiosyncratic API of each package (you’d still need to read their docs to see the parameters specification, though), mlS3 wraps them under a common wrap_* / predict() pattern.

What You’ll Learn

• How to install and load mlS3 (for now, from GitHub)
• How to apply a consistent API across multiple ML algorithms for both classification and regression tasks

Models Covered

Datasets Used

• iris — binary and multiclass classification (setosa/versicolor, all three species)
• mtcars — regression to predict miles per gallon (mpg)

Key Design Principle

All models follow the same two-step workflow:

mod  <- wrap_*(X_train, y_train, ...)       # Train
pred <- predict(mod, newx = X_test, ...)    # Predict

This makes it easy to swap algorithms and compare results without rewriting your pipeline.

Prerequisites

• R with the following packages: remotes, caret, randomForest, ggplot2
• mlS3 installed from GitHub (for now) via remotes::install_github("Techtonique/mlS3")

Code

Install packages

install.packages(c("remotes"))

install.packages(c("caret"))

install.packages(c("randomForest"))

remotes::install_github("Techtonique/mlS3")

Predefined wrappers

# Classification

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


# =============================================================================
# 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")



Accuracy (glmnet):  1 
Accuracy (lightgbm):  0.2444444  # I'm probably doing something wrong here
Accuracy (ranger):  0.9333333 
Accuracy (svm):  0.9333333 
RMSE (lightgbm):  4.713678 
RMSE (glmnet):  2.972557 
RMSE (svm):  2.275837 
RMSE (ranger):  2.067692 

caret wrapper

For this part, you need to install package caret and randomForest. Model parameters available for caret: https://topepo.github.io/caret/available-models.html

library(mlS3)
library(caret)

# ============================================================================
# Regression with mtcars dataset
# ============================================================================
data(mtcars)

# Prepare data
X_reg <- mtcars[, -1]  # All except mpg
y_reg <- mtcars$mpg     # Target variable

# 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]

# ----------------------------------------------------------------------------
# Example 1: Random Forest with specific parameters
# ----------------------------------------------------------------------------
cat("\n=== Example 1: Random Forest Regression ===\n")

mod_rf <- wrap_caret(X_reg_train, y_reg_train,
                     method = "rf",
                     mtry = 3)        # Number of variables sampled at each split

print(mod_rf)

# Predictions
pred_rf <- predict(mod_rf, newx = X_reg_test)
rmse_rf <- sqrt(mean((pred_rf - y_reg_test)^2))
r2_rf <- 1 - sum((y_reg_test - pred_rf)^2) / sum((y_reg_test - mean(y_reg_test))^2)

cat("RMSE:", round(rmse_rf, 3), "\n")
cat("R-squared:", round(r2_rf, 3), "\n")


=== Example 1: Random Forest Regression ===
$model
Random Forest 

22 samples
10 predictors

No pre-processing
Resampling: None 

$task
[1] "regression"

$method
[1] "rf"

$parameters
$parameters$mtry
[1] 3


attr(,"class")
[1] "wrap_caret"
RMSE: 2.007 
R-squared: 0.681 

library(ggplot2)

df <- data.frame(
  pred = pred_rf,
  actual = y_reg_test
)

ggplot(df, aes(x = pred, y = actual)) +
  geom_point() +
  geom_abline(slope = 1, intercept = 0, color = "red") +
  theme_minimal() +
  labs(x = "Predicted", y = "Actual")