What is the role of feature scaling in neural networks?

Feature scaling is a preprocessing step that standardizes or normalizes the range of input features in a dataset. In neural networks, its primary role is to ensure that all input features contribute equally during training, which improves the stability and speed of the optimization process. Without scaling, features with larger numerical ranges can dominate the learning process, causing the model to converge slowly or produce suboptimal results. For example, if one feature ranges from 0 to 1 and another from 0 to 10,000, the larger feature’s gradient updates may overshadow the smaller one, skewing the model’s focus during training.

A key benefit of feature scaling is its impact on gradient-based optimization algorithms like stochastic gradient descent (SGD). When features are on similar scales, the loss landscape becomes smoother and more symmetrical, allowing the optimizer to find the minimum efficiently. For instance, in a neural network predicting house prices, features like square footage (e.g., 500–5,000) and number of bedrooms (e.g., 1–5) would have vastly different scales. Scaling these to a common range (e.g., 0–1) ensures that weight updates for both features are balanced. Additionally, activation functions like sigmoid or tanh, which saturate when inputs are too large, benefit from scaled features by staying in their active regions, preventing vanishing gradients.

While feature scaling is generally recommended, its necessity can vary. For example, architectures with built-in normalization layers (e.g., Batch Normalization) may reduce the need for explicit scaling. However, preprocessing inputs remains a good practice, as it simplifies the initial learning phase. Methods like standardization (subtracting the mean and dividing by the standard deviation) or min-max scaling (mapping values to a 0–1 range) are widely used. In cases where features have outliers, robust scaling techniques (e.g., using median and interquartile range) might be preferable. Ultimately, feature scaling is a low-effort step that often leads to faster convergence, better generalization, and more stable training in neural networks.