What is Lasso Regression?

Lasso regression adds a penalty term to ordinary least squares, shrinking less important feature coefficients to zero for variable selection. It aids in building simpler models and mitigating multicollinearity, balancing model simplicity with predictive accuracy. Popular in predictive modeling, it’s effective with large feature sets.

In the world of data analysis and prediction, regression techniques are essential for understanding relationships between variables and making accurate forecasts. One standout method among many is Lasso regression. It not only helps in finding these relationships but also aids in creating models that are easier to interpret and more resilient. However in R Programming Language dealing with imbalanced data or when some data points are more crucial than others, traditional Lasso regression might fall short. That’s where Weighted Lasso Regression steps in. It offers a more sophisticated way of modeling by assigning different levels of importance to various data points.