Linear Regression

Classic ordinary least squares regression that fits a straight line through the data.

When to use:

• First model to try - serves as a strong baseline
• When you need interpretable results and coefficients
• Linear relationships between features and target
• Quick predictions needed

Strengths: Very fast, highly interpretable, works well with many features, no hyperparameters to tune Weaknesses: Assumes linear relationships, sensitive to outliers, no built-in regularization (can overfit)

Model Parameters

Fit Intercept (default: true) Whether to calculate intercept term. Keep true unless data is centered at zero.