Back to GalleryLogistic Regression
Logistic Regression maps linear combinations of features to probabilities via the sigmoid function, outputting a value between 0 and 1. The model is optimized using gradient descent on binary cross-entropy loss, resulting in a single linear decision boundary.
Key Parameters & Visual Influence:
• Learning Rate & Epochs: Control the speed and stability of the boundary's convergence.
• Regularization: L1 (Lasso) promotes sparsity by zeroing out weaker weights (performing feature selection), whereas L2 (Ridge) shrinks weights uniformly to prevent overfitting.
• Decision Threshold: Shifting this moves the linear boundary closer to one class or the other, adjusting the precision-recall trade-off.
Dataset Studio
CSV format: Headers in first row, last column = label/target. All values must be numeric.
Example: x1, x2, label