IBM Data Science Practice Test 2026 – Comprehensive Exam Prep

Feature scaling is the process of normalizing or standardizing the values of features (or variables) in a dataset to ensure that each feature contributes equally to model training and prediction. This is particularly important in machine learning algorithms that compute distances (like k-nearest neighbors or support vector machines) or that can be sensitive to the relative scale of data (like gradient descent optimization).

When features have different scales, those with larger values can dominate the distance calculations and may lead to skewed results in model performance. For example, if one feature ranges from 0 to 1 while another ranges from 0 to 1,000, the larger range will overshadow the smaller one, thus preventing the model from learning effectively from the feature distributions.

By applying normalization (scaling features to a range of 0 to 1) or standardization (scaling features to have a mean of 0 and a standard deviation of 1), we mitigate these issues, allowing each feature to contribute fairly to the analysis. This can lead to improved model convergence and overall better performance, making it a crucial step in the data preprocessing phase.

The other options do not accurately describe the purpose of feature scaling. Boosting dimensionality, feature selection, and visualizing feature distributions