Beginner
Introduction to R for Machine Learning
Explore the machine learning ecosystem in R, compare key frameworks, and set up your ML environment.
The ML Ecosystem in R
R has a rich ecosystem for machine learning, with multiple frameworks and hundreds of algorithm implementations:
| Framework / Package | Focus | Status |
|---|---|---|
| tidymodels | Modern, tidy ML framework (recommended) | Actively developed |
| caret | Unified interface to 200+ models | Maintenance mode |
| mlr3 | Object-oriented ML framework | Actively developed |
| randomForest | Random forest implementation | Stable |
| xgboost | Gradient boosting | Actively developed |
| glmnet | Regularized regression (lasso, ridge) | Stable |
| keras / torch | Deep learning | Actively developed |
R vs Python for ML
| Aspect | R | Python |
|---|---|---|
| Statistical ML | Excellent (deep statistical roots) | Good (scikit-learn) |
| Deep learning | torch, keras (smaller community) | PyTorch, TensorFlow (dominant) |
| Model interpretation | Excellent (built-in summaries) | SHAP, LIME packages |
| Visualization | ggplot2 (superior for EDA) | matplotlib, seaborn |
| Production deployment | plumber, vetiver | Flask, FastAPI (more mature) |
| GPU support | torch package | Native PyTorch/TF support |
Setting Up Your ML Environment
R
# Core ML packages install.packages(c( "tidymodels", # Modern ML framework "ranger", # Fast random forests "xgboost", # Gradient boosting "glmnet", # Regularized regression "kernlab", # SVM "rpart", # Decision trees "dbscan", # DBSCAN clustering "factoextra", # Cluster visualization "Rtsne", # t-SNE "caret", # Alternative ML framework "vip" # Variable importance plots )) # For GPU support (optional) install.packages("torch")
Prerequisites
Before starting: This course assumes you have completed Basics of R and R for Data Science, or have equivalent experience with R programming, the tidyverse, and ggplot2.
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