A model is a function that takes an input and produces a prediction Let's consider two possible models for this data: 1. M-00-x+5 2. M-2x+1 Compute the predictions of models and for the values in x. These predictions should be vectors of the same shape as-Then plot the prediction lines of these two models overlayed on the "observed" data (xv). Use pit.plot() to draw the lines. Note: you will generate only one plot. Make sure to include axes, titles and legend. YOUR CODE HERE Evaluation Metrics How good are our models? Intuitively, the better the model, the more closely it fits the data we have. That is, for each well compare y, the true value, with the predicted value. This comparison is often 7 called the loss or the error. One common such comparison is squared error.. Averaging over all our data points, we get the mean squared error: MSE-300 Exercise 3: Computing MSE (25 points) Write a function for computing the MSE metric and use it to compute the MSE for the two models above, M. and M.