A one-page dimensionality reduction reference, every snippet explained line by line: variance and missing-value filters, correlation pruning, leak-free selection, RFE, tree importance, Lasso, consensus voting, and PCA for reduction and compression.
The article details the benefits of using XGBoost for tabular data, covering its efficient workflow, API usage, model training, cross-validation, early stopping, regularisation, and optimal tuning strategies.
This content provides definitions and explanations of various terms related to machine learning and artificial intelligence, covering topics like model training, evaluation metrics, architectures, and optimisation techniques.
August 5, 2026
15 min read
Discover more from Discuss Data Science, Machine Learning and Analytics
Subscribe now to keep reading and get access to the full archive.
[…] Dimensionality Reduction Cheatsheet […]
[…] Cheatsheet: https://datalad.co.uk/dimensionality-reduction-in-python-cheatsheet/ […]