After adding the x^3 feature to the training set and plotting it, the text says, “Gradient descent has emphasized the data that is the best fit to the x^2 data by increasing the w_1 term relative to the others.” I believe this should be, '…increasing the w_2 term relative to the others."? This is the model at this stage, “0.08𝑥+0.54𝑥2+0.03𝑥3+0.0106”.
Thank-you.
Hi @chantrym
You are correct, based on the image above (taken from course slides), the weight indices start from 1 and 0.54 is the coefficient w_2 associated with x^2. So, it must be w_2 instead of w_1.
Thanks for reporting the issue!
Would you mind taking a look at this? @balaji.ambresh
Thanks for considering me, @Alireza_Saei
Unfortunately, I didn’t take up the new version of MLS. Adding @rmwkwok and @TMosh since it’s unclear to me on how the weights are organized.
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Hello, @Alireza_Saei, in the " Selecting Features" section, in the first paragraph, the equation at the end states that they are associating w_1 with the squared term.
Hi @rmwkwok
Thank you for the clarification. Based on the image shared from the course slides, I thought the weight indices start from 1 (or we assign w_0 to the bias term).
Thanks again!
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My apologies. Thank-you for clarifying.
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