Linear Regressor

Linear Regression is the most fundamental regression algorithm. It models the relationship between features and a target value by fitting a straight line (or hyperplane) that minimizes the sum of squared errors.

It is the simplest, fastest, and most interpretable regression model always a good starting point.

Linear Regression has no hyperparameters to tune, making it extremely easy to use. After training, each coefficient directly tells you how much the target changes when that feature increases by one unit.

Always try Linear Regression first as a baseline.

Use Cases

• Baseline model: in any AEC prediction workflow.
• Quick cost estimation: based on simple geometric quantities.
• Energy demand approximation: in early design phase.
• Sensitivity analysis: Understanding how each design parameter affects the output.