Data Science, Python, Machine Learning, Dimensionality Reduction.
comparison to PCA and t-SNE, UMAP offers a good balance of accuracy, efficiency, and scalability, making it a popular choice for dimensionality reduction in machine learning and data analysis. umap_model = umap.UMAP(n_components = 2, n_neighbors = 10, random_state = 42) components =