Basic Math Concepts – Lasso Regression
| Topic | Why It’s Needed | Simple Explanation |
|---|---|---|
| 1. Linear Equations | Understand how a model makes predictions | Like: y = mx + c — Lasso extends this to many variables |
| 2. Coordinate Geometry | Visualize data and lines | Helps us to see how the model fits a line to points |
| 3. Basic Algebra | Rearranging, solving equations | To follow how weights are updated |
| 4. Mean and Squared Error (MSE) | Measures prediction error | We must know how average and square differences work |
| 5. Absolute Value | Central to L1 penalty | Lasso adds penalty using ` |
| 6. Concept of Slope/Gradient | Core of gradient descent (learning process) | Understand how models “learn” by reducing errors |
| 7. Optimization Idea | Know what “minimizing loss” means | We’re trying to find the best model by minimizing a cost |
| 8. Graph Interpretation (Optional) | Helpful to see how weights behave | Plotting and visual reasoning improve intuition |
Next – Elasticnet Regression