AI Engineering Degree Practice Exam 2026 - Free AI Engineering Practice Questions and Study Guide

Normalization is often performed before applying regression algorithms primarily to standardize the scale of feature values. When datasets contain features measured on different scales, the inherent differences in scale can cause regression models to become biased towards features with larger ranges.

For example, imagine a dataset where one feature ranges from 1 to 1000 and another feature ranges from 0 to 1. Without normalization, the larger range could disproportionately influence the model's predictions. By normalizing the features, each feature is scaled to a similar range (typically between 0 and 1 or standardized to have a mean of 0 and a variance of 1), allowing the regression algorithm to treat all features equally. This standardization facilitates quicker convergence during training and often leads to improved predictive performance, especially in algorithms that rely on distance calculations, such as linear regression.

The other choices do not accurately reflect the reasoning for normalization. Increasing the variability of the data does not address the issue of scales. Removing outliers is a separate preprocessing step that may or may not be necessary, dependent on the specific dataset. Ensuring all values are integers is not a goal of normalization; in fact, normalization typically involves working with continuous values.