Question 4
Consider a regression problem where you are tasked with predicting the sale prices of houses based on their square footage. You decide to experiment with two different models:
The training dataset consists of information on 200 houses, and you use the models to make predictions on a test dataset of 50 houses. The Mean Squared Error (MSE) is chosen as the evaluation metric.
Based on the above data, answer the given subquestions.
Identify the factors that could influence the model’s performance on the training dataset in this housing price prediction scenario. Select all correct statements:
Model P may struggle to capture non-linear relationships present in house price data.
Model Q might be sensitive to outliers in the square footage variable.
The choice between Model P and Model Q depends on the budget constraints of potential homebuyers.
Model Q will always perform well on the test dataset.