IBM Data Science Test 2025 – 400 Free Practice Questions to Pass the Exam

"Scaling" data refers to the process of adjusting the range of the data features to a standard scale, ensuring that different features contribute equally to analysis and model performance. This is particularly important in machine learning algorithms that rely on distance calculations, like k-nearest neighbors or support vector machines, as well as gradient descent-based optimization.

When data features have different scales—such as one feature ranging from 0 to 1 and another from 0 to 1000—the larger scale can disproportionately influence the model's learning process. By scaling features to a uniform range, typically between 0 and 1 or by standardizing them to have a mean of 0 and a standard deviation of 1, we promote a more balanced and efficient learning environment.

This adjustment helps in improving the convergence speed of certain algorithms and can enhance the overall performance of machine learning models by reducing potential bias stemming from the differing scales of features.