In this question, how we can get the X matrix for value M= 6.

You will have to train and compare several regression models to illustrate the trade-off between overfitting and underfitting. You will use data generated synthetically. The prediction is to be performed based on only one feature, denoted by . The target t is a noisy measurement of the function ftrue (x) = sin(4x). Thus, t satisfies the following relation t = sin(4x) + ftrue (I) (1) where is random noise with a Gaussian distribution with 0 mean and variance 0.09. = 0, z (10) Construct a training set consisting of only 10 examples {(r(), t()),..., (x(10), t(10))}, where x(),..., (10) are uniformly spaced in the interval [0, 1], with () and t(),..., t(0) are generated using relation (1). Construct a validation set consisting of 100 examples with the feature z uniformly spaced in the interval [0, 1] and targets generated randomly according to relation (1). Similarly, construct a test set with 100 examples. You may use the code provided on the next page. When generating the random data use a four-digit number containing the last 4 digits of your student ID (in any order), as seed for the pseudo number generator. = 1, You have to train ten regression models of increasing capacity (corresponding to M from 0 to 9) and record and compare their training and validation errors. For each M, 0 M9, the predictor has the form fM(x) = wo+wx +wx + + WMIM. = Note that, when M 0, the predictor is just a constant function. For each M you have to train the predictor using least squares (i.e., use squared error as loss function) and record the training and validation root mean squared errors. Then plot the