UMAP

Q: How deeply does Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI cover UMAP?

Defines nonlinear projection, applications, and caveats, but does not explain UMAP’s algorithm or implementation.

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

UMAP is a nonlinear dimensionality-reduction technique based on manifold-learning principles that constructs a low-dimensional representation of high-dimensional data.

UMAP is a nonlinear dimensionality-reduction technique that projects complex, high-dimensional data into a few dimensions for visualization. section 2.4.3

In exploratory data analysis, UMAP can expose patterns, clusters, and isolated points that are difficult to see across many original features. section 2.4.3

Use UMAP with these practical cautions:

Compare its projections with alternatives such as PCA, SVD, and t-SNE when investigating nonlinear structure. section 2.4.3
Treat apparent clusters and anomalies as exploratory signals, because hyperparameter changes can produce substantially different plots. section 2.4.3
Avoid treating the chart as a complete explanation when many points or dense clusters make interpretation difficult. section 2.4.3

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

liveBook manning.com

Read Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

Data Without LabelsUniform Manifold Approximation and Projection (UMAP) is a nonlinear manifold-learning technique for dimensionality reduction that constructs a low-dimensional embedding while preserving data relationships.
liveBook manning.com

Graph Neural Networks in ActionUMAP is a nonlinear dimensionality-reduction technique that projects high-dimensional data into a lower-dimensional space while attempting to preserve local and global structure.

Compare how 3 books explain UMAP →

Related concepts

dimensionality reductionkind of · Dimensionality reduction is the transformation of data into a representation with fewer dimensions while retaining selected information, structure, or relationships.
principal component analysisalternative to · Principal component analysis is a linear dimensionality-reduction technique that transforms data into orthogonal components ordered by the variance they capture.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.