How can you improve a linear regression model that is underfitting?

How can you improve a linear regression model that is underfitting?

Step 1: Identify that your linear regression model is underfitting by checking its performance metrics (like R-squared) and observing the residuals.
Step 2: Look at your dataset and see if there are additional features (variables) that could help explain the target variable better.
Step 3: Add these new features to your dataset. This could include interaction terms, polynomial features, or other relevant variables.
Step 4: Re-train your linear regression model using the updated dataset with the new features.
Step 5: Evaluate the performance of the new model to see if it has improved and is capturing the patterns in the data better.

Underfitting – Underfitting occurs when a model is too simple to capture the underlying trends in the data, leading to poor performance.
Feature Engineering – Adding more features or improving existing ones can help the model learn more complex patterns and improve its performance.
Model Complexity – Increasing the complexity of the model can help it fit the training data better, reducing underfitting.