Lasso Regression Explained

Lasso regression works similarly to ridge regression, but with one key difference. Instead of just shrinking the impact of less useful features, it can remove them completely.

Think of packing a suitcase for a short trip. You go through every item and decide what truly matters. You leave anything unnecessary behind entirely, not just pack it smaller.

Lasso regression does the same thing with features. It reduces the weight of unimportant variables to zero, effectively removing them from the model.

How lasso regression helps

• It automatically eliminates features that add little or no value.
• This makes the final model simpler and easier to interpret.
• It works especially well when a dataset has many irrelevant or redundant features.

Real-world example

Imagine predicting a student's exam score using twenty different factors. Some, like study hours, genuinely matter.

Others, like their favorite color, add nothing useful. Lasso regression would remove such irrelevant factors completely.