IBM Data Science Practice Test 2026 – Comprehensive Exam Prep

Feature selection is a critical step in the data science process, primarily because it helps to improve model accuracy and reduce overfitting. When a model is trained on a large number of features, especially those that may not be relevant or informative, it can capture noise rather than the underlying pattern in the data. This often leads to overfitting, where the model performs well on training data but poorly on unseen data.

By selecting only the most relevant features, you can simplify the model's complexity, enhancing its generalization capabilities. With fewer features, the model can focus on the most important signals in the data, leading to better performance and interpretability. Additionally, reducing the number of features can streamline the training process, thereby saving computational resources and time.

The other choices don't align with the primary objectives of feature selection. Increasing model complexity goes against the goal of improving accuracy and reducing overfitting. Maximizing the number of features used can lead to complications and does not necessarily benefit the model's performance. Moreover, while simplifying data preprocessing may be a byproduct, it is not the main reason for feature selection. The focus is firmly on improving model performance and ensuring that the model remains robust and generalizable.