Machine Learning Frameworks in R

R
Machine Learning
Published

April 12, 2026

R’s ecosystem offers a rich selection of machine learning frameworks, each with distinct design philosophies and strengths. This post is a side-by-side comparison of five ML frameworks in R that provide unified interfaces over multiple algorithms, with runnable code examples on the same dataset so you can compare APIs directly. The focus is on packages that let you swap algorithms without rewriting your code.

Frameworks at a Glance

Feature tidymodels caret mlr3 h2o qeML
Built-in tuning ✅ (tune) ✅ ✅ (mlr3tuning) ✅ (AutoML) ✅ (qeFT())
Preprocessing pipeline ✅ (recipes) ✅ (preProcess) ✅ (mlr3pipelines) ✅ ❌
Model variety 200+ engines 230+ methods 100+ learners GBM, GLM, DL, DRF 20+ wrappers
Relative speed Moderate Moderate Moderate Fast (distributed) Depends on backend
Learning curve Medium Low High Low Very low
Active development ✅ ⚠️ Maintenance mode ✅ ✅ ✅
Best for Production pipelines Quick prototyping Benchmarking AutoML & scale Teaching & exploration

Setup and Data

ALl examples below uses the iris classification task: predict Species from the four numeric measurements. A single train/test split is created up front so results are directly comparable.

library(dplyr)

# Reproducible train/test split (framework-agnostic)
set.seed(42)
n <- nrow(iris)
train_idx <- sample(seq_len(n), size = floor(0.7 * n))

train_data <- iris[train_idx, ]
test_data  <- iris[-train_idx, ]

# Store accuracy results for final comparison
results <- data.frame(
  Framework = character(),
  Model = character(),
  Accuracy = numeric(),
  stringsAsFactors = FALSE
)

cat("Training set:", nrow(train_data), "observations\n")
Training set: 105 observations
cat("Test set:    ", nrow(test_data), "observations\n")
Test set:     45 observations

1. tidymodels

The tidymodels ecosystem is the modern, tidyverse native approach to modeling in R. It provides a consistent grammar for specifying models (parsnip), preprocessing (recipes), composing workflows (workflows), and tuning hyperparameters (tune).

library(tidymodels)

# Define a recipe (preprocessing)
rec <- recipe(Species ~ ., data = train_data)

# Define a model specification
rf_spec <- rand_forest(trees = 500) %>%
  set_engine("ranger") %>%
  set_mode("classification")

# Combine into a workflow
rf_wf <- workflow() %>%
  add_recipe(rec) %>%
  add_model(rf_spec)

# Fit the workflow
rf_fit <- rf_wf %>% fit(data = train_data)

# Predict on test set
preds_tidy <- predict(rf_fit, test_data) %>%
  bind_cols(test_data %>% select(Species))

# Evaluate
acc_tidy <- accuracy(preds_tidy, truth = Species, estimate = .pred_class)
acc_tidy
# A tibble: 1 × 3
  .metric  .estimator .estimate
  <chr>    <chr>          <dbl>
1 accuracy multiclass     0.978
results <- rbind(results, data.frame(
  Framework = "tidymodels",
  Model = "Random Forest (ranger)",
  Accuracy = acc_tidy$.estimate
))
TipKey Strengths
  • Composable pipeline: recipe + model + workflow is easy to extend
  • Swap engines with one line (set_engine("xgboost"))
  • Seamless cross-validation and hyperparameter tuning via tune_grid() / tune_bayes()
  • Deep integration with the tidyverse

2. caret

The caret package (Classification And REgression Training) was the de facto standard for ML in R for over a decade. It wraps 230+ models behind a single train() interface. While now in maintenance mode (its creator, Max Kuhn, leads tidymodels), it still remains widely used.

library(caret)

# Train a random forest with 5-fold CV
ctrl <- trainControl(method = "cv", number = 5)

rf_caret <- train(
  Species ~ .,
  data = train_data,
  method = "rf",
  trControl = ctrl,
  tuneLength = 3  # Try 3 values of mtry
)

# Best tuning parameter
rf_caret$bestTune
  mtry
1    2
# Predict on test set
preds_caret <- predict(rf_caret, test_data)

# Evaluate
cm_caret <- confusionMatrix(preds_caret, test_data$Species)
cm_caret$overall["Accuracy"]
 Accuracy 
0.9555556 
results <- rbind(results, data.frame(
  Framework = "caret",
  Model = "Random Forest (rf)",
  Accuracy = as.numeric(cm_caret$overall["Accuracy"])
))
TipKey Strengths
  • Minimal API: a single train() call handles preprocessing, tuning, and fitting
  • 230+ model methods available out of the box
  • Built-in confusionMatrix() with extensive diagnostics
  • Massive community knowledge base and Stack Overflow coverage

3. mlr3

mlr3 is a modern, object-oriented ML framework built on R6 classes. It excels at systematic benchmarking, composable pipelines, and reproducible experiments. The learning curve is steeper, but the payoff is a powerful, extensible architecture.

library(mlr3)
library(mlr3learners)

# Define the task
task <- TaskClassif$new(
  id = "iris",
  backend = train_data,
  target = "Species"
)

# Define the learner
learner <- lrn("classif.ranger", num.trees = 500)

# Train
learner$train(task)

# Predict on test data — create a test task to avoid backend storage issues
test_task <- TaskClassif$new(
  id = "iris_test",
  backend = test_data,
  target = "Species"
)
pred_mlr3 <- learner$predict(test_task)

# Evaluate
acc_mlr3 <- pred_mlr3$score(msr("classif.acc"))
acc_mlr3
classif.acc 
  0.9555556 
results <- rbind(results, data.frame(
  Framework = "mlr3",
  Model = "Random Forest (ranger)",
  Accuracy = as.numeric(acc_mlr3)
))
TipKey Strengths
  • R6 object-oriented design — everything is an object with methods
  • First-class benchmarking: compare multiple learners on multiple tasks with benchmark()
  • Composable pipelines via mlr3pipelines (stacking, ensembling, feature engineering)
  • Built-in resampling strategies and performance measures

4. h2o (AutoML)

h2o is a distributed machine learning platform with a powerful R interface. Its standout feature is h2o.automl() automatic model selection, hyperparameter tuning, and stacked ensemble creation with a single function call. It runs on a local JVM, so Java must be installed.

Note

This section requires Java (JDK 8+) to be installed. h2o starts a local JVM-based server. If you don’t have Java, skip to the results comparison — the other four frameworks cover the same ground without this dependency.

library(h2o)

# Start a local h2o cluster (uses available cores)
h2o.init(nthreads = -1, max_mem_size = "2G")

H2O is not running yet, starting it now...

Note:  In case of errors look at the following log files:
    C:\Users\RIDDHI~1\AppData\Local\Temp\Rtmp8G4u9C\filec0caad7dcc/h2o_Riddhiman_Roy_started_from_r.out
    C:\Users\RIDDHI~1\AppData\Local\Temp\Rtmp8G4u9C\filec0c1660783f/h2o_Riddhiman_Roy_started_from_r.err


Starting H2O JVM and connecting:  Connection successful!

R is connected to the H2O cluster: 
    H2O cluster uptime:         2 seconds 276 milliseconds 
    H2O cluster timezone:       Asia/Kolkata 
    H2O data parsing timezone:  UTC 
    H2O cluster version:        3.44.0.3 
    H2O cluster version age:    2 years, 3 months and 23 days 
    H2O cluster name:           H2O_started_from_R_Riddhiman_Roy_axb153 
    H2O cluster total nodes:    1 
    H2O cluster total memory:   1.98 GB 
    H2O cluster total cores:    24 
    H2O cluster allowed cores:  24 
    H2O cluster healthy:        TRUE 
    H2O Connection ip:          localhost 
    H2O Connection port:        54321 
    H2O Connection proxy:       NA 
    H2O Internal Security:      FALSE 
    R Version:                  R version 4.5.3 (2026-03-11 ucrt) 
h2o.no_progress()  # Suppress progress bars

# Convert data to h2o frames
train_h2o <- as.h2o(train_data)
test_h2o  <- as.h2o(test_data)

# Run AutoML — automatic model selection and stacking
aml <- h2o.automl(
  x = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"),
  y = "Species",
  training_frame = train_h2o,
  max_models = 10,
  seed = 42
)

22:11:38.3: AutoML: XGBoost is not available; skipping it.
22:11:39.171: _min_rows param, The dataset size is too small to split for min_rows=100.0: must have at least 200.0 (weighted) rows, but have only 105.0.
# Leaderboard — best models ranked by cross-validated performance
h2o.get_leaderboard(aml) |> as.data.frame() |> head(5)
                                                 model_id mean_per_class_error
1    DeepLearning_grid_1_AutoML_1_20260412_221137_model_1           0.03988095
2                          GBM_2_AutoML_1_20260412_221137           0.05029762
3                          GLM_1_AutoML_1_20260412_221137           0.05029762
4    StackedEnsemble_AllModels_1_AutoML_1_20260412_221137           0.05982143
5 StackedEnsemble_BestOfFamily_1_AutoML_1_20260412_221137           0.05982143
     logloss      rmse        mse
1 0.10262590 0.1802887 0.03250400
2 0.13688347 0.1981121 0.03924839
3 0.09073184 0.1736624 0.03015862
4 0.12933104 0.2002635 0.04010548
5 0.11828660 0.1921656 0.03692762
# Predict with the best model
preds_h2o <- h2o.predict(aml@leader, test_h2o)
acc_h2o <- mean(as.vector(preds_h2o$predict) == as.vector(test_h2o$Species))
cat("Accuracy:", acc_h2o, "\n")
Accuracy: 0.9777778 
results <- rbind(results, data.frame(
  Framework = "h2o",
  Model = paste0("AutoML (", aml@leader@algorithm, ")"),
  Accuracy = acc_h2o
))

# Shutdown h2o
h2o.shutdown(prompt = FALSE)
TipKey Strengths
  • h2o.automl() — fully automatic model selection, tuning, and stacked ensembles
  • Trains GBM, XGBoost, GLM, DRF, and deep learning models in one call
  • Distributed computing — scales to datasets larger than memory
  • Built-in leaderboard for model comparison
  • Production deployment via MOJO/POJO model export

5. qeML

qeML (Quick and Easy Machine Learning) takes a different approach of minimizing boilerplate. Every algorithm — random forest, gradient boosting, SVM, KNN, LASSO, neural nets, and more is wrapped behind a one-line qe*() function with a consistent (data, targetName) signature. No formula objects, no matrix conversions, no separate predict calls just results. It’s ideal for teaching, exploration, and quick model comparisons.

library(qeML)

# qeML convention: pass full data + target name (string)
# It handles train/test splitting internally via holdout
# But to match our split, we'll train on train_data and predict on test_data
# predict() expects new data WITHOUT the target column
test_features <- test_data[, -which(names(test_data) == "Species")]

# Random Forest (wraps randomForest)
rf_qe <- qeRF(train_data, "Species")
preds_rf_qe <- predict(rf_qe, test_features)
acc_rf_qe <- mean(preds_rf_qe$predClasses == test_data$Species)
cat("Random Forest accuracy:", acc_rf_qe, "\n")
Random Forest accuracy: 0.9777778 
# Gradient Boosting (wraps gbm)
gb_qe <- qeGBoost(train_data, "Species")
preds_gb_qe <- predict(gb_qe, test_features)
acc_gb_qe <- mean(preds_gb_qe$predClasses == test_data$Species)
cat("Gradient Boosting accuracy:", acc_gb_qe, "\n")
Gradient Boosting accuracy: 0.9555556 
# SVM (wraps e1071)
svm_qe <- qeSVM(train_data, "Species")
preds_svm_qe <- predict(svm_qe, test_features)
acc_svm_qe <- mean(preds_svm_qe$predClasses == test_data$Species)
cat("SVM accuracy:", acc_svm_qe, "\n")
SVM accuracy: 0.9555556 
# Use the best-performing qeML model for the results table
best_acc_qe <- max(acc_rf_qe, acc_gb_qe, acc_svm_qe)
best_model_qe <- c("Random Forest", "Gradient Boosting", "SVM")[
  which.max(c(acc_rf_qe, acc_gb_qe, acc_svm_qe))
]

results <- rbind(results, data.frame(
  Framework = "qeML",
  Model = paste0(best_model_qe, " (qe wrapper)"),
  Accuracy = best_acc_qe
))
TipKey Strengths
  • One-line model fitting: qeRF(data, "target") — no formula, no matrix, no recipe
  • 20+ algorithms behind a uniform qe*() interface (RF, GBM, SVM, KNN, LASSO, neural nets, and more)
  • qeCompare() lets you benchmark multiple methods in a single call
  • Built-in holdout evaluation
  • Lowest learning curve of any framework showcased here

Results Comparison

All five frameworks were trained on the same 70/30 split of the iris dataset. Here’s how they stack up:

library(knitr)

results <- results %>% arrange(desc(Accuracy))
kable(results, digits = 4, caption = "Test Set Accuracy by Framework")
Test Set Accuracy by Framework
Framework Model Accuracy
tidymodels Random Forest (ranger) 0.9778
h2o AutoML (deeplearning) 0.9778
qeML Random Forest (qe wrapper) 0.9778
caret Random Forest (rf) 0.9556
mlr3 Random Forest (ranger) 0.9556

On a clean, small dataset like iris, accuracy differences are minimal. The real differentiator is the API and workflow each framework provides. On real-world datasets the choice of framework matters more for how you structure your code than for raw accuracy.

Closing thoughts

There is no single “best” ML framework in R and the right choice depends on the task at hand:

  • Start with tidymodels for a modern, composable, production-ready pipeline.
  • Try qeML for the fastest path from data to results.
  • Use h2o for automatic model selection and stacking with minimal effort.
  • Consider mlr3 for rigorous benchmarking and advanced pipeline composition.
  • Stick with caret if maintaining existing code or prefer its battle-tested simplicity.