Feature Scaling and Normalization #ai #machinelearning #preprocessing

5 Jun 2025 · 1 min

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1:12

In this instructional segment, the speaker outlines two core preprocessing techniques—feature scaling and normalization—essential for delivering high‑quality data to machine learning models. Feature scaling adjusts feature values to comparable ranges, addressing issues where disparate magnitudes can degrade performance of magnitude‑sensitive algorithms such as K‑Nearest Neighbors, Support Vector Machines, and neural networks. The speaker details two prevalent scaling methods: min‑max scaling, which compresses values into a bounded interval (commonly 0‑1 or –1‑1), and standardization (z‑score normalization), which re‑centers data to a mean of zero and rescales to unit variance. Normalization is presented as a special case of scaling that reshapes data to follow a normal (Gaussian) distribution, thereby facilitating faster convergence for gradient‑descent‑based models by mitigating scaling‑related errors.

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Feature scaling and normalization are 2 techniques widely used in preprocessing, which is a key step in presenting high-quality data to the machine learning model. Feature scaling aims to transform feature values so as to place them in similar scales. Indeed, features often have values that are too far from each other. This can affect the performance of some models which are sensitive to data magnitude, like KNN and SVM, or even neural networks. Feature scaling solves this problem using 2 very common common techniques: min-max scaling, which squeezes values into a limited interval, often between -1 and 1 or between 0 and 1, and standardization, also known as z-score normalization, which transforms values so that they have a mean of 0 and a standard deviation of 1. Normalization is a special case of feature scaling. It transforms values so that they follow the normal distribution, in other words, a probability distribution symmetrical around the mean. This enables model based on gradient descent to converge quickly to the optimized state, avoiding errors linked to the values scaling.