Which term describes selecting the key features that contribute to a machine learning model's predictions?

Feature selection is the process of identifying and selecting the most relevant features from a dataset that contribute significantly to making predictions in a machine learning model. This step is crucial because it helps improve the model's performance by reducing overfitting, decreasing training time, and enhancing the accuracy of predictions. By focusing on the key features, you streamline the model to only utilize the most pertinent information, leading to clearer insights and better decision-making.

In contrast, model validation pertains to assessing how well the chosen model generalizes to unseen data, ensuring its effectiveness and reliability. Data augmentation involves creating additional training samples by altering the existing data, which enhances the robustness of the model but does not directly relate to selecting features. Model deployment refers to the process of integrating a machine learning model into an existing production environment, enabling it to be used for real-world applications. Therefore, feature selection is the term that specifically addresses the identification of essential features for model predictions.