RT Avi Chawla (@_avichawla)Bias-variance tradeoff has a missing detail!Not many ML engineers know about it.Consider fitting a polynomial regression model on some dummy dataset, say, y=sin(x) + noise.As shown in the first plot in the image, as we increase the degree (m):- The training loss will go down to zero.- The test (or validation) loss will decrease and then increase.But notice what happens as we continue to increase the degree (m):↳ Test loss decreases again (shown in the second plot)This is called th ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/pFxYUsWxlvAvi Chawla (@_avichawla):Bias-variance tradeoff has a missing detail!Not many ML engineers know about it.Consider fitting a polynomial regression model on some dummy dataset, say, y=sin(x) + noise.As shown in the first plot in the image, as we increase the degree (m):- The training loss will go https://t.co/BIdCfkZRHO ...

Bias-variance tradeoff has a missing detail!Not many ML engineers know about it.Consider fitting a polynomial regression model on some dummy dataset, say, y=sin(x) + noise.As shown in the first plot in the image, as we increase the degree (m):- The training loss will go down to zero.- The test (or validation) loss will decrease and then increase.But notice what happens as we continue to increase the degree (m):↳ Test loss decreases again (shown in the second plot)This is called the “double descent phenomeno ...