Download the seeds_dataset.txt data set from the book’s GitHub site, which contains 210 independent examples. The categorical output (response) here is the type of wheat grain: Kama, Rosa, and...

Download the seeds_dataset.txt data set from the book’s GitHub site, which contains 210 independent examples. The categorical output (response) here is the type of wheat grain: Kama, Rosa, and Canadian (encoded as 1, 2, and 3), so that c = 3. The seven continuous features (explanatory variables) are measurements of the geometrical properties of the grain (area, perimeter, compactness, length, width, asymmetry coefficient, and length of kernel groove). Thus, x ∈ R
⁷
(which does not include the constant feature 1) and the multi-logit pre-classifier in Example 9.2 can be written as g(x) = softmax(Wx + b), where W ∈ R
^3×7
and b ∈ R 3 . Implement and train this pre-classifier on the first n = 105 examples of the seeds data set using, for example, Algorithm 9.4.1. Use the remaining n ′ = 105 examples in the data set to estimate the generalization risk of the learner using the crossentropy loss. [Hint: Use the cross-entropy loss formulas from Example 9.4.]