In this week's #TidyTuesday video, I show how to improve a model's predictive power using ensemble learning with the Stacks package. I first go into creating diverse models for stacking and blending #TidyModels using the Recipe package. I create a PCA regression, Spline, RandomForest, and XGBoost model and then use Stacks to stack the model predictions to evaluate Sale Price in the Ames Housing dataset. I then go on to show how to manually create a stacked ensemble model using Tidymodels and create a better ensemble model that outperforms the sub-models and previous stacked ensemble model from the Stacks package.
#MachineLearning
Connect with me on LinkedIn: https://www.linkedin.com/in/andrew-couch/
Q&A Submission Form: https://forms.gle/6EzU4GCR9VnJx8gg7
Code for this video: https://github.com/andrew-couch/Tidy-Tuesday/blob/master/Season%201/Scripts/TidyModelsStacks.Rmd
Stacks Package: https://github.com/tidymodels/stacks
TidyTuesday: https://github.com/rfordatascience/tidytuesday