Which of the following is crucial for improving a model's predictive accuracy?

Feature selection and engineering is essential for improving a model's predictive accuracy because it focuses on identifying and selecting the most relevant features or variables from the dataset that contribute to the predictive power of the model. By concentrating on these key features, the model can better capture the underlying patterns in the data, which leads to more accurate predictions.

Good feature engineering involves transforming raw data into formats that are more suitable for modeling, which can include creating new features, normalizing data, and encoding categorical variables. This process helps to reduce noise, eliminate redundancy, and ensure that the model uses the most informative inputs. When the right features are chosen and crafted, the complexity of the model can be reduced, leading to improved performance, reduced overfitting, and better generalization to unseen data.

The other options may not contribute positively to the model's predictive accuracy. Collecting more irrelevant data can introduce noise and complicate the learning process. Ignoring data quality can lead to inaccuracies and unreliable predictions, as poor-quality data can severely impact model performance. Reducing computing power could limit the ability to train complex models or process data efficiently, which can hinder the accuracy of predictions.