Databricks Certified Machine Learning Associate — Question 45

A data scientist is developing a machine learning model to predict house prices in a competitive real estate market. They initially select a loss function that heavily penalizes large errors, hoping it will improve the model’s performance. However, after training, they observe that the model struggles to converge and produces unstable predictions, with large variations in price predictions for similar houses. The data scientist suspects that the chosen loss function is causing these issues.

Why is it crucial to select the right loss function in this situation?

Answer options

• A. The loss faction ensures that the data is balanced.
• B. The loss function directly influences how the model's parameters are updated during training.
• C. The loss function controls the size of the training set.
• D. The loss function determines the computational efficiency of the model.

Correct answer: B

Explanation

Choosing the correct loss function is essential because it dictates how the model's parameters are updated during training, which affects the learning process and convergence. If a loss function penalizes large errors too heavily, it can lead to instability in predictions, as observed in the example. The other options do not accurately describe the role of the loss function in model training.