Linear Vs Logistic Regression Explained
This animation contrasts linear regression and logistic regression by showing how each fits data: a straight line minimizing squared error for continuous outputs, and an S-shaped sigmoid curve mapping inputs to probabilities for binary classification. It visualizes the cost surfaces, gradient descent updates, and decision boundaries side by side, highlighting why squared error suits regression while log-loss suits classification. Useful for undergraduates learning core supervised learning models before moving to neural networks.
Narrated · 16:9 · every frame verified for overlaps, spacing and edges before rendering
The prompt that made it
I am teaching my students about linear regression and logistic regression in machine learning, they are undergraduates
Make the next one in this series
- Show how gradient descent updates weights step by step for logistic regression
- Compare decision boundaries for logistic regression versus a linear classifier on the same dataset
- Visualize why log-loss is preferred over squared error for classification problems
