Matheus Nogueira·23h agoSearch, Shrink or Compress: Three Ways to Rescue Linear Regression from Too Many FeaturesToo many features break linear regression. Best subset, ridge, lasso and PCR each fix it by trading a little bias for much less variance.
Akshat Gupta·2d agoPCA — Principal Component AnalysisPCA is an unsupervised feature extraction technique used specifically for creating new unattached sets of components/ features/sets from…
NTTP·6d agoThe Mag7 is really 40% Mag1 + 6 othersAnd they don’t split like you might think they wouldA response icon1A response icon1
Pragathi Velu·Sep 23Principal Component Analysis — How AI Simplifies Complexity Without Losing What MattersSeries 3, Part 9: ML Algorithms, Simply Explained
Aimyo·Aug 27Principle Component AnalysisA recap of Andrew Ng course (Machine Learning Specialization) on Coursera
Arko Sengupta·Aug 22Principal Component Analysis: A Comprehensive GuideExplore detailed Principal Component Analysis
Diane Khambu·Aug 17Learning Principal Component Analysis (PCA) on my Own TermsI was a bit surprised that I came across a note on my notebook that had these:
Hamza Ahmed·Aug 13Advanced Data Reshaping with K-Means and PCAIn the previous article, we covered baseline scoring, mutual information, and simple transforms like ratios, counts, and group…
InCode AppliedbyGustavo R Santos·Aug 10PCA Beyond Dimensionality Reduction: 3 Practical Uses for Data ScienceHow to leverage Principal Component Analysis for feature selection, pattern discovery, and composite ranking models.A response icon4A response icon4
Gregory Sen·Jul 6Rotating the Black Box: What PCA and ICA Actually Do to Embedding PredictorsA follow-up to Turning Marketing Copy into Model Predictors.
