r-ml

name	r-ml
description	R machine learning packages. Use for classification, regression, clustering, deep learning, gradient boosting (xgboost, lightgbm), random forests, neural networks, and time series forecasting.

R Machine Learning Skill

Sub-skills

Sub-skill	Description
r-ml-frameworks	tidymodels, caret, mlr3, h2o
r-ml-boosting	xgboost, lightgbm, gbm
r-ml-trees	randomForest, ranger, rpart
r-ml-regularization	glmnet, lasso, elastic-net
r-ml-deeplearning	torch, keras, neural networks
r-ml-timeseries	prophet, fable, forecast
r-ml-survival	survival, survminer
r-ml-anomaly	AnomalyDetection, anomalize

Machine learning and predictive modeling in R.

ML Frameworks

Package	Description
caret ★	Classification and Regression Training
mlr3 ★	Next-gen extensible ML framework
tidymodels ★	Tidyverse-friendly modeling
h2o	Deep learning, RF, GBM, GLM

Gradient Boosting

Package	Description
xgboost ★	eXtreme Gradient Boosting
lightgbm ★	Light Gradient Boosting Machine
gbm	Generalized Boosted Regression
bst	Gradient Boosting
mboost	Model-Based Boosting
CoxBoost	Cox models boosting
GAMBoost	GAM boosting
gamboostLSS	GAMLSS boosting
GMMBoost	Mixed models boosting

Tree-Based Methods

Package	Description
randomForest	Breiman's random forests
ranger ★	Fast random forests
randomForestSRC	RF for survival/regression/classification
rpart	Recursive partitioning trees
party	Recursive partitioning lab
partykit	Partitioning toolkit
C50	C5.0 Decision Trees
Cubist	Rule-based regression
evtree	Evolutionary trees
tree	Classification/regression trees
bigrf	Big Random Forests

Regularization & Linear Models

Package	Description
glmnet ★	Lasso and elastic-net GLMs
lars	Least Angle Regression, Lasso
elasticnet	Elastic-Net, Sparse PCA
penalized	L1/L2 penalized estimation
ncvreg	SCAD/MCP regularization
grplasso	Group Lasso
grpreg	Grouped covariates regularization
L0Learn	Best subset selection

Neural Networks & Deep Learning

Package	Description
torch ★	PyTorch-like tensors/NNs
MXNet	Flexible GPU deep learning
nnet	Feed-forward NNs
RSNNS	Stuttgart NN Simulator
keras	Keras interface

Mixed Effects Models

Package	Description
lme4 ★	Mixed-effects models
nlme	Mixed-effects with custom covariance
glmmTMB	Generalized mixed-effects

Time Series & Forecasting

Package	Description
prophet ★	Facebook's forecasting tool
fable	Tidy forecasting
forecast	Time series forecasting

Anomaly Detection

Package	Description
AnomalyDetection	Twitter's anomaly detection
anomalize	Tidy anomaly detection
BreakoutDetection	Twitter's breakout detection
CausalImpact	Google's causal inference

SVM & Kernel Methods

Package	Description
kernlab	Kernel-based ML lab
e1071	SVM, Naive Bayes, etc.
LiblineaR	Linear predictive models
svmpath	SVM path algorithm
penalizedSVM	Feature selection SVM

Clustering & Dimensionality

Package	Description
kohonen	Self-Organizing Maps
Rsomoclu	Parallel SOM
hda	Heteroscedastic Discriminant Analysis
klaR	Classification and visualization

Feature Selection

Package	Description
Boruta	All-relevant feature selection
FSelector	Subset-search/ranking selection
varSelRF	Variable selection with RF

Survival Analysis

Package	Description
survival ★	Survival analysis
survminer	Survival visualization
ahaz	Additive hazards regression

Other

Package	Description
arules	Association rules mining
rattle	GUI for data mining
rminer	Simplified NN/SVM usage
ROCR	Classifier performance visualization
SuperLearner	Ensemble learning
RWeka	Weka interface

Quick Examples

# tidymodels workflow
library(tidymodels)
split <- initial_split(df, prop = 0.8)
train <- training(split)
test <- testing(split)

model <- rand_forest(trees = 100) %>%
  set_engine("ranger") %>%
  set_mode("classification")

recipe <- recipe(target ~ ., data = train) %>%
  step_normalize(all_numeric())

workflow <- workflow() %>%
  add_model(model) %>%
  add_recipe(recipe)

fit <- workflow %>% fit(data = train)
predict(fit, test)

# xgboost
library(xgboost)
dtrain <- xgb.DMatrix(data = as.matrix(train_x), label = train_y)
model <- xgb.train(
  params = list(objective = "binary:logistic", max_depth = 6),
  data = dtrain, nrounds = 100
)

# caret
library(caret)
ctrl <- trainControl(method = "cv", number = 5)
model <- train(target ~ ., data = train, method = "rf", trControl = ctrl)

Resources

• tidymodels: https://www.tidymodels.org/
• caret: https://topepo.github.io/caret/
• xgboost: https://xgboost.readthedocs.io/
• mlr3: https://mlr3.mlr-org.com/