Context: In machine learning, developing models that accurately generalize to new data is crucial. However, a common issue encountered is underfitting, where models fail to capture the underlying patterns in the data due to their simplicity.
Problem: Underfitting results in poor training and test dataset performance, indicating that the model needs to learn from the data adequately. This can have significant negative impacts in practical applications, where accurate…