Min-Max Scaling
Min-Max Scaling, also known as normalization, is a data preprocessing technique that transforms numerical features to a fixed range, typically [0, 1]. It works by subtracting the minimum value of the feature and dividing by the range (max - min), ensuring all values are scaled proportionally. This method is commonly used in machine learning to handle features with different scales and improve model performance.
Developers should use Min-Max Scaling when working with machine learning algorithms that are sensitive to feature scales, such as gradient descent-based models (e.g., neural networks, support vector machines) or distance-based algorithms (e.g., k-nearest neighbors). It is particularly useful for datasets where features have varying ranges, as it prevents features with larger magnitudes from dominating the model, leading to faster convergence and better accuracy. However, it is not recommended for data with outliers, as they can distort the scaling.