16.8 Uniform Manifold Approximation and Projection (UMAP)

• Non-linear, like ICA
• Powerful: divides the group a lot
• Uses distance-based nearest neighbor to find local areas where data points are more likely related
• Creates smaller feature set
• Unsupervised and supervised versions
• Can be sensitive to tuning parameters

library(embed)
bean_rec_trained %>%
  step_umap(all_numeric_predictors(), num_comp = 4) %>%
  plot_validation_results() +
  ggtitle("UMAP (unsupervised)")

bean_rec_trained %>%
  step_umap(all_numeric_predictors(), outcome = "class", num_comp = 4) %>%
  plot_validation_results() +
  ggtitle("UMAP (supervised)")

The supervised method looks to perform better.