Regularización Ridge y Lasso

Plain linear regression can chase noise, growing huge coefficients that fit training data yet fail on new data. That trap is called overfitting. 😬

Regularization fixes this by adding a penalty for large coefficients. The model now balances fitting the data against keeping weights small.

Ridge penalizes the squared size of the coefficients. It pulls every weight toward zero but rarely makes any exactly zero.

Lasso penalizes the absolute size instead. This can drive weak coefficients all the way to zero, dropping those features entirely.

Because Lasso zeroes weak weights, it doubles as feature selection. The surviving non-zero coefficients are the ones it found useful.

The alpha knob controls how hard the penalty pushes. Bigger alpha means stronger shrinkage and a simpler, smoother model.

Crank alpha too high and coefficients shrink so far the model ignores real signal. That opposite mistake is called underfitting.

Penalties compare coefficients directly, so features must share a scale first. Standardize your data or the penalty hits big-unit features unfairly.

Use Ridge when most features help a little, and Lasso when you suspect many are useless and want a sparse, lean model.

Can't decide? ElasticNet mixes the Ridge and Lasso penalties, giving you shrinkage and some feature dropping at the same time.

Never guess alpha by hand. Try a range and pick the value that scores best on held-out folds with cross-validation.

One key difference sets these two apart in practice.

Regularization tames overfitting: Ridge shrinks weights, Lasso can zero them, and alpha sets the strength. Scale first, then tune. 🎯