.

.

Feature Scaling is important in Machine Learning due to the following reasons:

Equal Feature Influence: Scaling guarantees that all features contribute equally to model training by putting them into a comparable numeric range. This avoids models from being biased toward bigger scale features, which would otherwise dominate the learning process.

Enhanced Algorithm Performance: Scaling features improves the performance of several machine learning algorithms, such as SVMs, KNN, and neural networks. It increases convergence rates in optimization processes such as gradient descent, resulting in faster and more reliable model training.

Accurate Distance Calculations: To compute distances accurately, algorithms that use distance measures, such as KNN and clustering, require scaled features. Unscaled features with greater ranges may confuse distance computations and impact clustering or classification results.

Stable Gradient Descent: Gradient-based optimization methods perform better on scaled data. Uniformly scaled features aid gradient descent algorithms in navigating to the ideal solution without oscillating or taking wasted steps.

Effective Regularization: To avoid overfitting, techniques like L1 and L2 regularization penalize large coefficients. Scaling guarantees that regularization penalties are paid evenly to all features.