A Practical Guide to Dimensionality Reduction Techniques
summary
Step‑by‑step procedures for reducing 1,933 features down to a usable set with Python code and concrete outcomes: missingness flags, low‑variance drops, pairwise correlation and condition‑index removal, PCA for unsupervised use, feature‑agglomeration, forward/backward/stepwise, LASSO and tree‑based importance. Uses scikit‑learn NumPy Pandas snippets on a 145,000‑row dataset, reports a best model around AUC 0.72 versus a kitchen‑sink 0.63, and cautions that PCA underperforms for supervised prediction while LASSO and trees give practical ranked feature lists.
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chapters (15)
- 0:00Introduction
- 1:29Dimensionality reduction
- 9:05Twelve techniques overview
- 10:30Handling missing values
- 12:59Feature variation checks
- 14:06Correlation and multicollinearity
- 17:43Condition index for multicollinearity
- 20:11Principal Component Analysis
- 21:55PCA components to keep
- 23:19Cluster analysis for variables
- 27:18Forward/backward/stepwise selection
- 31:01Lasso regression
- 32:27Random forest importance
- 33:11Model performance comparison
- 34:14Summary
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