IBM Data Science Practice Test 2026 – Comprehensive Exam Prep

Feature selection is crucial in model construction because it helps to improve model accuracy and reduces overfitting. When you select the most relevant features from your dataset, the model is better able to learn the underlying patterns without being distracted by noise or irrelevant information that can arise from unnecessary features.

By reducing the number of features, you not only make the model simpler but also enhance its generalization capabilities. A model that is too complex may fit the training data very well but then perform poorly on unseen data due to overfitting. This is where feature selection plays a key role—it helps streamline the model by focusing only on the most predictive features, which leads to better performance on new data.

Moreover, with fewer features, the model's training time is reduced, making it more efficient. Thus, feature selection is an important step to ensure that your model is both accurate and robust.