How to do lasso regression in R

Lasso regression is a type of linear regression that adds a regularization term to the equation. This helps to prevent overfitting, which occurs when a model is too complex and is able to fit the noise in the data instead of the actual underlying trend. The “lasso” part of the name comes from the fact that this regularization term is a type of constraint on the size of the coefficients in the equation, similar to how a lasso rope is used to constrain a wild animal.

To perform lasso regression in R, you will first need to install the “glmnet” package. This package contains the necessary functions to perform lasso regression in R.

Next, you will need to prepare your data. This includes splitting it into a training set and a testing set, as well as standardizing the variables if necessary.

Once your data is ready, you can use the “glmnet” function to fit a lasso regression model to your training data. This function has several options that you can use to control the behavior of the model, such as the regularization parameter.

After the model is trained, you can use it to make predictions on new data. You can also use the “coef” function to view the estimated coefficients of the model.

Finally, it is important to evaluate the performance of the model, for this you can use the “predict” function and compare the predicted values with the actual values from the testing set.

It’s important to note that Lasso Regression is a technique that is used to select the most important features of a dataset for Linear Regression, in other words it helps you to find the features that have the biggest impact on your output variable.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: How to do lasso regression in R.