Ridge Regression (L2 Regularization) in Python

[@АлександрТрубников-о3ж]

Thanks for the video. In fact, matrix differentiation is usually used in the prod, rather than a closed matrix form.

[@mayankkumar-hk9vx]

how to calculate R^2 value in this model?

[@peterl9966]

nice video, but for L2 shouldn't theta be: theta = inv(xTx + N*lam*I) * xTy , where N = X.shape[0]

[@EndlessEngineering]

N = X.shape[0] would be the total number of samples in the X matrix. Notice that I am stacking the samples vertically with X = np.vstack(). You want the number of columns in X, which represents the dimension of the input.

[@prathmeshtripathi]

Gem 😍😍

[@EndlessEngineering]

Thank you for watching! I am glad you found it useful

[@0xNameless]

Something off with the Audio... Its uncomfortable to listen to, potentially hurting your views.