Feature Scaling

Feature scaling is a preprocessing step used to transform numerical features so that they are on a similar scale. This is a crucial step for many machine learning algorithms that are sensitive to the magnitude of features. For instance, algorithms that use distance calculations, like K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and clustering algorithms, can be heavily biased by features with larger scales. If one feature ranges from 0 to 1000 and another from 0 to 1, the algorithm will naturally give more weight to the first feature, which is often not desirable. There are two primary methods for feature scaling. The first is Normalization, also known as Min-Max Scaling. This technique rescales the features to a fixed range, usually between 0 and 1. The formula is (x - min(x)) / (max(x) - min(x)). Normalization is useful when you need your data to be within a specific bounded interval. The second method is Standardization, or Z-score normalization. This technique transforms the data to have a mean of 0 and a standard deviation of 1. The formula is (x - mean(x)) / std_dev(x). Standardization does not bind values to a specific range, which makes it less sensitive to outliers. It is the more commonly used scaling technique in machine learning. By applying feature scaling, you ensure that all features contribute more equally to the model's learning process, often leading to faster convergence and better overall performance.