Hello @Smit_Shah1,

Note that the features are normalized independently, meaning that in some scenario, one feature can be scaled down by 1000 times, and another feature can be scaled up just 2 times, **since they are not always scaled by the same amount, we can’t expect the ratio to be maintained**.

Now, I need you to do the thinking to get the answer for yourself. I will give you the first steps, and the final step, but you will need to fill in the middle by yourself. Note that thinking takes time, and I won’t be able to respond to you any time soon, so take your time.

This is the original formula generating `y_train`

:

y = w_1x_1 + w_2x_2 + b , where w_1=2, w_2=3, b=4 in your case, but we will stick with the symbols instead of the actual values for now.

Then you did the normalization by converting x_1 to x_{1, norm}, and x_2 to x_{2,norm}. Each feature’s normalization has two relevant factors - a mean \mu and a standard deviation \sigma.

x_{1, norm} = \frac{x_1 - \mu_1}{\sigma_1}

x_{2, norm} = \frac{x_2 - \mu_2}{\sigma_2}

By now, you have 3 equations.

On the other hand, your model fits to normalized features, meaning that it is:

y = w_{1, norm}x_{1, norm} + w_{2, norm}x_{2, norm} + b_{norm}

Your task is to find out the relation formula between w_{1, norm} and w_1, and the relation formula between w_2 and w_{2,norm}, and the relation formula between b_{norm} and b.

Please write down those formulae with the symbols but **not** their actual values.

With these formulae, you should be able to evaluate the expected weights and bias (which are w_{1, norm}, w_{2, norm}, b_{norm} from the known w_1, w_2, b which are just 2, 3, 4 respectively and the known means and standard deviations.

I won’t respond to this thread for at least the next 2 days unless you are able to share the correct relation formulae and the correct expected weights and bias.

Cheers,

Raymond

We will get into that after the relation fomulae.