Lasso Regression is a popular technique in machine learning that can be used for feature selection and regularization. It is a regression method that uses L1 regularization to shrink the coefficients of less important features to zero, effectively removing them from the model. This can improve the model's performance by reducing overfitting and improving interpretability.
If you are new to Lasso Regression, there are several resources available to help you get started. In this blog post, we will explore some of the best resources for beginners.
Lasso regression is a commonly used technique in machine learning for feature selection and regularization. Here are some resources to get started with Lasso regression: