Linear Regression & Regularization (Ridge, Lasso, ElasticNet)
Ordinary Least Squares, Feature Selection via L1 Sparsity, and L2 Variance Shrinkage
#Regression#OLS#Ridge#Lasso#ElasticNet#Regularization
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STAGE 1 / 7— Anti-Pattern
Section 1: The LLM Anti-Pattern vs Right-Sized Model
The Naive Generative LLM Approach:
Asking an LLM to predict housing prices, customer lifetime value (LTV), or financial quarterly revenue based on numerical tabular columns.
Why It Fails in Production:
LLMs are terrible at arithmetic interpolation, fail on tabular scale, cannot guarantee monotonic trends, and hallucinate numerical estimates.
Targeted Algorithm (Linear Regression & Regularization (Ridge, Lasso, ElasticNet))
Latency:0.001ms
Cost / 1M Ops:$0.00
Determinism:Analytically Closed-Form
Generative LLM Alternative
Latency:1,500ms
Cost / 1M Ops:$4,000
Determinism:Inconsistent Number Guessing