dp-100-data-scientist-assoc video for context: Scaling numerical features is a crucial step in preprocessing data for machine learning. Input features often
Context: Scaling numerical features is a crucial step in preprocessing data for machine learning. Input features often vary significantly in range, and many machine learning algorithms are sensitive to the magnitude of these features. Without feature scaling, features with larger magnitudes may receive higher weights, regardless of their actual importance in predicting the output. Question: Which scaling approach is best described by the following? "A method that mathematically rescales the data so that it has a mean of 0 and a standard deviation of 1."