Which steps must the data scientist take to improve model accuracy?

Explanations:

Increasing the amount of regularization typically helps to prevent overfitting but may lead to underfitting if the model is already underperforming. Since the model has poor accuracy on both training and test datasets, this step is unlikely to help improve accuracy.

Decreasing the amount of regularization can allow the model to better fit the training data, potentially improving its ability to capture relevant patterns in the data, thus enhancing performance on both the training and test datasets.

Increasing the number of training examples can help improve the model’s accuracy by providing more data for the model to learn from, allowing it to better generalize to unseen data. This can also help reduce overfitting if the original dataset was small.

Increasing the number of test examples does not improve model training or accuracy; it merely provides a more reliable estimate of model performance. The goal is to improve model performance through training adjustments rather than expanding the test dataset.

Increasing the number of model features can provide the model with more information, allowing it to capture complex relationships within the data, which can improve accuracy. However, this must be done cautiously to avoid introducing noise or overfitting.

Decreasing the number of model features may lead to underfitting if the model is already not capturing enough information from the data. Since the initial model has poor accuracy, reducing features might further degrade performance unless it addresses specific irrelevant features.