Building ML Workflows with tidymodels
A comprehensive guide to creating reproducible machine learning workflows using the tidymodels ecosystem in R.
RMachine Learningtidymodels
Introduction
tidymodels provides a consistent interface for machine learning workflows in R, making it easier to build, tune, and evaluate models.
Building a Workflow
R
library(tidymodels)
# Define model
rf_spec <- rand_forest(trees = 1000) %>%
set_engine("ranger") %>%
set_mode("classification")
# Create workflow
rf_workflow <- workflow() %>%
add_recipe(recipe_spec) %>%
add_model(rf_spec)
# Fit and evaluate
rf_fit <- rf_workflow %>%
fit(data = training_data)Model Evaluation
Learn how to assess model performance using tidymodels' evaluation metrics and visualization tools.