Now, let's fit our updated linear regression model using the ordinary least squares estimator! We will start you off with something simple by using only 2 features: the number of bedrooms in the household and the log-transformed total area covered by the building (in square feet). Consider the following expression for our 1st linear model that contains one of the features: Log Sale Price = 90 + 01 - (Bedrooms) In parallel, we will also consider a 2nd model that contains both features: Log Sale Price = 00 + 91 - (Bedrooms) + 92 - (Log Building Square Feet) Without running any calculation or code, complete the following statement by filling in the blank with one of the comparators below: || IA IV Suppose we quantify the loss on our linear models using MSE (Mean Squared Error). Consider the training loss of the 1st model and the training loss of the 2nd model. We are guaranteed that: Training Loss of the 1stMode1 Training Loss of the 2nd Model