Can UMAP project new, unseen data points?

Yes, unlike t-SNE, UMAP provides a transform() method that allows you to project new data into an existing embedding space based on a previously trained model.

Does UMAP support supervised learning?

Yes, UMAP can incorporate label information (y-values) to guide the dimensionality reduction process, resulting in cleaner class separation while maintaining internal structure.

How does UMAP compare to t-SNE for visualization?

UMAP is generally faster, scales better to large datasets, and is significantly better at preserving the global structure of the data compared to t-SNE.

Can I use this skill for clustering applications?

Yes, it includes specific best practices for using UMAP as a preprocessing step for density-based clustering algorithms like HDBSCAN, including parameter recommendations for n_neighbors and min_dist.

What is Parametric UMAP?

Parametric UMAP uses a neural network to learn the mapping, allowing for faster transformations of new data and the ability to use inverse transforms (decoders) to reconstruct original data.

UMAP Dimension Reduction

Name: UMAP Dimension Reduction
Author: K-Dense-AI

byK-Dense-AI

•

3,719

データサイエンスとML

Performs fast, scalable non-linear dimensionality reduction for visualization, clustering, and high-dimensional data analysis.

概要

This skill enables Claude to implement and tune UMAP (Uniform Manifold Approximation and Projection) for complex data science tasks. It provides optimized patterns for preserving topological structure in 2D/3D visualizations, preprocessing data for density-based clustering like HDBSCAN, and utilizing supervised learning to guide embedding creation. Whether you are performing exploratory data analysis or building robust feature engineering pipelines, this skill ensures best practices for parameter tuning, data scaling, and seamless scikit-learn integration.

主な機能

Global and local structure preservation for high-dimensional data
Parametric UMAP implementation for neural network integration
Fast and scalable non-linear manifold learning
Support for supervised and semi-supervised dimensionality reduction
Optimized preprocessing configurations for HDBSCAN clustering
3,719 GitHub stars

ユースケース

Metric learning and feature engineering for downstream machine learning classifiers
Generating 2D or 3D visualizations of high-dimensional biological or financial datasets
Preprocessing features to overcome the curse of dimensionality for clustering

概要

主な機能

Global and local structure preservation for high-dimensional data
Parametric UMAP implementation for neural network integration
Fast and scalable non-linear manifold learning
Support for supervised and semi-supervised dimensionality reduction
Optimized preprocessing configurations for HDBSCAN clustering
3,719 GitHub stars

ユースケース

Metric learning and feature engineering for downstream machine learning classifiers
Generating 2D or 3D visualizations of high-dimensional biological or financial datasets
Preprocessing features to overcome the curse of dimensionality for clustering