Basic Math Concepts – ElasticNet Regression

1. Algebra (Linear Equations)

What to Know Why It’s Needed Analogy
Variables, constants, coefficients Understand how each feature contributes to output Like a recipe: flour × 2 + sugar × 1.5 = cake weight
Solving linear equations ElasticNet is a linear model under the hood We’re solving for weights that best fit the output

2. Statistics

What to Know Why It’s Needed Analogy
Mean, median, variance To understand the spread of data and prediction errors Like knowing if house prices in an area vary a lot
Correlation between variables Multicollinearity detection — Ridge helps here If two features say the same thing, shrink them together

3. Linear Regression Basics

What to Know Why It’s Needed Analogy
y = mx + c (line of best fit) Core idea of ElasticNet is improving this with regularization Fitting a line to predict house price by size
Error = (Predicted – Actual)² To minimize this error in training Like guessing a weight and correcting your guess each time

4. Concept of Optimization

What to Know Why It’s Needed Analogy
What is gradient descent? ElasticNet updates weights by minimizing loss Like rolling a ball downhill to reach the lowest point (minimum error)
How to adjust weights using gradients To reduce prediction error step-by-step We adjust the oven temperature until the cake is just right

5. Regularization (Basic Idea)

What to Know Why It’s Needed Analogy
L1 (absolute value) vs L2 (squared value) penalties ElasticNet blends both L1 is like firing unhelpful team members, L2 is like telling them to calm down

6. Basic Calculus (Intuition Only)

What to Know Why It’s Needed Analogy
Idea of derivatives (slope of a function) Gradient descent uses this to minimize error Like checking how steep a hill is while hiking
Not the formulas, but knowing that slope = rate of change Helps understand “why weights are updated” Steeper slope = bigger correction

Elasticnet Regression – Summary