> ## Documentation Index
> Fetch the complete documentation index at: https://shapely.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# 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.
### An example of test
