Agent skill · data analytics · aj-geddes
Dimensionality Reduction
Reduce feature dimensionality using PCA, t-SNE, and feature selection for feature reduction, visualization, and computational efficiency
Why this skill is useful
Provides specific implementations and techniques for PCA and t-SNE that enhance the AI's ability to perform dimensionality reduction effectively.
What it needs
Requires matplotlib, scikit-learn, seaborn installed locally. About 3k tokens when loaded. Last updated 2026-03-04. 307 stars on the source repository.
What this skill does
Dimensionality Reduction Overview Dimensionality reduction techniques reduce the number of features while preserving important information, improving model efficiency and enabling visualization of high-dimensional data. When to Use High-dimensional datasets with many features Visualizing complex datasets in 2D or 3D Reducing computational complexity and training time Removing redundant or highly correlated features Preventing overfitting in machine learning models Preprocessing data before clustering or classification Techniques PCA: Principal Component Analysis t-SNE: t-Distributed Stochastic Neighbor Embedding UMAP: Uniform Manifold Approximation and Projection Feature Selection: Selecting important features Feature Extraction: Creating new features Benefits Reduce computational complexity Remove noise and redundancy Improve model generalization Enable visualization Prevent curse of dimensionality Implementation with Python Algorithm Comparison PCA: Linear, fast, interpretable t-SNE: Non-linear, good visualization, computationally expensive UMAP: Non-linear, preserves local/global structure Feature Selection: Maintains interpretability Factor Analysis: Statistical approach Choosing Number of Components Explained Variance: Retain 95% of variance Elbow Method: Look for "elbow" in scree plot Cross-validation: Optimize for downstream task Deliverables Scree plots and cumulative variance 2D/3D visualizations PCA loadings interpretation Feature importance ranking Model performance comparison Component interpretation
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aj-geddes/dimensionality-reduction