R Ml

R Machine Learning Skill

Sub-skills

Machine learning and predictive modeling in R.

ML Frameworks

Gradient Boosting

Tree-Based Methods

Regularization & Linear Models

Neural Networks & Deep Learning

Mixed Effects Models

Time Series & Forecasting

Anomaly Detection

SVM & Kernel Methods

Clustering & Dimensionality

Feature Selection

Survival Analysis

Other

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/

Source: LeoLin990405/r-analytics-skill — distributed by TomeVault.