Hello, I'm Ganesh. I'm building git-lrc, an AI code reviewer that runs on every commit. It is free, unlimited, and source-available on Github. Star git-lrc on GitHub to help more developers discover the project. Do give it a try and share your feedback for improving the product.
Slope-Intercept Form equation
A residual is the difference between the actual y value and the predicted y value.
Residule = (Actual Y - Predicted Y)
Finally, the residual is squared, and the sum of all the residuals is calculated, which is called the Mean Squared Error (MSE).
(Residual)^2 is nothing but the vertical distance between the predicted line and the actual data points, squared.
The lesser the value, the greater the accuracy.
Using this Mean Squared Error (MSE), we can adjust the slope and y-intercept to minimize the error.
Conclusion
Will discuss Gradient Descent in the next part.
Any feedback or contributors are welcome! It’s online, source-available, and ready for anyone to use.
⭐ Star git-lrc on GitHub
Slope Intercept Form with Gradient Descent