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Statistical vs ML Models
Statistical Models
- Focus on inference -> understanding relationships between variables.
- Assume an underlying distribution (e.g., linear, normal).
- Typically work well with smaller datasets.
Goal: test hypotheses, estimate parameters.
Example: Linear regression to explain how income depends on education, experience, etc.
Machine Learning Models
- Focus on prediction -> finding patterns that generalize to unseen data.
- Fewer assumptions about data distribution.
- Can handle very large datasets and high-dimensional data.
Goal: optimize predictive performance.
Example: Random Forest predicting whether a customer will churn.
Key Similarities
Both use data to build models.
Both rely on training (fit) and evaluation (test).
Overlaps: linear regression is both a statistical model and an ML model, depending on context.
Book worth reading
The Manga Guide to Linear Algebra.
https://www.amazon.com/dp/1593274130
(Not an affiliate or referral)
On a lighter note
