Basic Math Concepts – Gradient Boosting Regression

Concept Why It’s Needed (In Simple Terms)
1. Averages / Mean Used to make the initial guess — we often start with the average of outputs.
2. Subtraction / Error To measure how far our guess is from the actual — this is the residual.
3. Simple Multiplication Needed to apply corrections and scale them (like using a learning rate).
4. Understanding “Trend” Helps to grasp how a tree or line can learn that as input increases, output increases.
5. Concept of “Iteration” Knowing we repeat a process multiple times — every step improves the result.
6. Linear relationship If we understand straight-line patterns (like y = mx), it helps in visualizing how each correction works.
7. Decision-making logic Like basic “if…then…” rules — decision trees split based on such logic.
8. Diminishing returns Concept that each round improves less than the last, helping to decide when to stop.

Gradient Boosting Regression – Gradient Boosting Regression Dataset Suitability Checklist