Linear Regression learns a straight-line relationship between input features and a numeric target. This guide connects the familiar line on the chart to the parameters and error values that actually produce it.
Open the interactive visualizer →The model combines input values with learned weights and a bias to make a numeric prediction. Training measures prediction error and changes the parameters so that the chosen objective decreases over recorded training states.
The main visualizer runs the implemented model in your browser and exposes the stored state, predictions, and metrics instead of substituting fabricated demo numbers.
Start with the default educational dataset, predict what will happen, run the model, then inspect one calculation or parameter at a time. For supported CSV workflows, local files are parsed and processed in the browser rather than uploaded by the application.