IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

Question: 1 / 400

What is feature selection in the context of data science?

The process of selecting a random set of features for use in model construction

The process of eliminating all features prior to model construction

The process of selecting a subset of relevant features for use in model construction

Feature selection refers to the process of identifying and selecting a subset of relevant features for use in model construction. This practice is crucial because not all features available in a dataset contribute positively to the model's performance. By focusing on relevant features, the model can become simpler, more efficient, and less prone to overfitting. Selecting the right features can also enhance the interpretability of the model, as it highlights the variables that have the most significant impact on the outcome.

By eliminating irrelevant or redundant features, data scientists improve the model's accuracy and performance. This selection process often involves statistical techniques to evaluate the importance of different features, ensuring that only those that meaningfully contribute to predictive power are included in the model.

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The process of using all available features in model construction

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