Scaling to Success: Implementing and Optimizing Penalized Models
This post will demonstrate the usage of Lasso, Ridge, and ElasticNet models using the Ames housing dataset. These models are particularly valuable when dealing with data that may suffer from multicollinearity. We leverage these advanced regression techniques to show how feature scaling and hyperparameter tuning can improve model performance. In this post, we’ll provide a step-by-step walkthrough on setting up preprocessing pipelines, implementing each model with scikit-learn, and fine-tuning them to achieve optimal results. This comprehensive approach not only aids in better prediction accuracy but also deepens your understanding of how different regularization methods affect model training and outcomes.
Let’s get started.
