Lasso Regression: Why the Solution Lives on a Diamond

Lasso regression is explained using the geometric intuition of vectors and projections. A standard linear regression model can overfit when it has enough features to perfectly match the training data, which leads to poor performance on new, unseen data. This overfitting is visualized as having enough feature vectors (directions) to reach the target vector exactly, eliminating the need for projection. Lasso is introduced as a technique to solve this problem by constraining the model's coefficients, which helps it generalize better instead of just memorizing the training set.