Microsoft Azure AI Fundamentals (AI-900) Practice Exam

What is the primary focus of feature selection in model training?

1. Improving data quality

2. Minimizing computational costs

3. Increasing model accuracy

4. Reducing overfitting

The correct answer is: Increasing model accuracy

Feature selection plays a crucial role in model training as it involves identifying and selecting the most relevant features from a dataset that contribute significantly to the predictive capacity of a model. By choosing only the most pertinent features, the model can more effectively learn the underlying patterns in the data, which often leads to increased model accuracy. The process of selecting the right features helps in enhancing the model's performance on unseen data by ensuring that it focuses on the attributes that have the greatest correlation with the target variable. This not only improves the accuracy of the model but can also lead to a more interpretable model by reducing the complexity and noise that irrelevant features may introduce. While feature selection can also help in minimizing computational costs and reducing overfitting, its primary focus is to enhance the accuracy of the model by ensuring that it is built on the most informative data elements. Therefore, increasing model accuracy is the central goal of feature selection in the context of training machine learning models.