(Solved): 2. Linear Regression with Closed Form Solution Bookmark this page Project due Oct 18, 2023 06:5 ...

2. Linear Regression with Closed Form Solution ? Bookmark this page Project due Oct 18, 2023 06:59 CDT After seeing the problem, your classmate Alice immediately argues that we can apply a linear regression model, as the labels are numbers from 0-9, very similar to the example we learned from Unit 1 . Though being a little doubtful, you decide to have a try and start simple by using the raw pixel values of each image as features. Alice wrote a skeleton code run_linear_regression_on_MNIST in main.py, but she needs your help to complete the code and make the model work. Closed Form Solution of Linear Regression $0.0/5.0$ points (graded) To solve the linear regression problem, you recall the linear regression has a closed form solution: $?={\left(X^{T}X+?I\right)}^{?1}{X}^{T}Y$ where $I$ is the identity matrix. Write a function parameter $?$. that computes this closed form solution given the features $X$, labels $Y$ and the regularization Available Functions: You have access to the NumPy python library as No need to import anything. 1 def closed_form( $X,Y$, lambda_factor): "in Computes the closed form solution of linear regression with L2 regularization Args: $x?(n,d+1)$ Numpy array (n datapoints each with $d$ features plus the bias feature in the first dimension) $?$ - $(n$, ) Numpy array containing the labels (a number from 0-9) for each data point lambda_factor - the regularization constant (scalar) Returns: theta - $(d+1$,$)Numpyarraycontainingtheweightsoflinearregression.Notethattheta[?]$ represents the $y$-axis intercept of the model and therefore $x[?]=1$ "n " \# YOUR CODE HERE raise NotImplementedError