IBM Data Science Practice Test 2025 – Comprehensive Exam Prep

Feature scaling is a crucial transformation technique commonly applied before using machine learning algorithms to ensure that the model performs effectively. This process involves adjusting the range of feature values so that they contribute equally to the calculation of distances and gradients during model training.

In many algorithms, particularly those that rely on distance metrics (like K-Nearest Neighbors and Support Vector Machines) or gradient-based optimization (like linear regression and neural networks), features with larger scales can disproportionately affect model outcomes. For example, if one feature is on a scale of 1 to 10 and another is on a scale of 1,000 to 10,000, the model may prioritize the latter simply due to its scale rather than its relevance.

By applying feature scaling techniques such as normalization (scaling features to a range of [0, 1]) or standardization (scaling to a mean of 0 and standard deviation of 1), each feature can contribute equally to the model's predictions. This not only improves the efficiency of the model training process but can also lead to better overall performance and accuracy.

Data removal, randomization, and data labeling do play important roles in the data preprocessing phase, but they do not specifically address the need for scale adjustments among features, which