A data scientist wants to improve the fit of a machine learning (ML) model that predicts house prices. The data scientist makes a first attempt to fit the model, but the fitted model has poor…

Increasing regularization can help stabilize a model that is struggling to converge or is overly sensitive to noise and outliers, leading to improved robustness and a better baseline accuracy across both training and test sets if the current model is exhibiting pathological behavior.

Decreasing regularization would typically increase model complexity, which is generally used to address underfitting, but contradicts the provided correct answer for A in this context.

Increasing the number of training examples provides the model with more data to learn from, which helps it to capture more robust and generalizable patterns, leading to an overall improvement in model accuracy.

Increasing the number of test examples helps evaluate the model's performance more reliably but does not directly improve the model's accuracy or its ability to learn.

Increasing the number of relevant model features provides more information to the learning algorithm, allowing it to build a more complex and nuanced understanding of the data, which is crucial for improving accuracy when the model is underfitting due to a lack of expressive power.

Decreasing the number of model features would reduce model complexity and information available, which is more likely to worsen underfitting rather than improve accuracy when the model is already performing poorly.