LASSO Regression in R

Statistical analysis frequently employs regression models. One such application is simulating the expected risk of a foreseeable event. Unfortunately, using standard regression techniques to create a model from a set of candidate variables often results in overfitting (i.e., adding too many variables) and overestimation (i.e., optimism bias) of the model's ability to explain the observed variability using the included variables. A kind of linear regression known as Lasso regression, or least absolute shrinkage and selection operator, incorporates a penalty proportional to the true amount of the magnitude of the coefficients. This penalty makes certain coefficients absolutely zero, allowing for variable selection and regularization to increase the model's interpretability and prediction accuracy.

Let's take an example where you have a dataset that contains several characteristics that might affect the price of a home, including the number of bedrooms, age, square footage, and proximity to schools. Based on these characteristics, you wish to develop a model that forecasts home values.
Lasso regression may aid you here to:

✓ Deal with situations where you have a lot of features, but not all of them are helpful.
✓ Accomplish feature selection by reducing the complexity of the model and lowering some coefficients to zero.
✓ Increase prediction accuracy by avoiding overfitting, particularly in cases where there are many features relative to the amount of data.

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