Back to GalleryLinear Regression
Linear Regression models the relationship between inputs and a continuous target using a straight line. It adjusts weights and biases via gradient descent to minimize Mean Squared Error (MSE).
Key Parameters & Visual Influence:
• Learning Rate & Epochs: Control the speed and precision of the regression line's convergence.
• Regularization: Adds L1 (Lasso) or L2 (Ridge) penalties to the loss function, constraining weight magnitudes and reducing the line's sensitivity to outliers.
Dataset Studio
CSV format: Headers in first row, last column = label/target. All values must be numeric.
Example: x1, x2, label