Feature scaling, also known as data normalization or standardization, is a preprocessing step in machine learning that involves transforming the input features to a similar scale. This is important because many machine learning algorithms are sensitive to the scale of input features. Feature scaling helps ensure that no single feature dominates the learning process due to having a larger scale than others. Here’s a comprehensive overview of feature scaling:
I. Why Feature Scaling is Important:
- Algorithm Sensitivity: Many machine learning algorithms, especially those that involve distance calculations or gradient-based optimization, are sensitive to the scale of input features.
- Equal Contribution of Features: Feature scaling ensures that all features contribute equally to the model fitting process. Without scaling, features with larger scales may dominate the learning process.
- Distance-Based Algorithms: Algorithms that rely on distance metrics, such as k-Nearest Neighbors (k-NN) and Support Vector Machines (SVM), can be influenced by the scale of features. Scaling helps in obtaining more meaningful distance measures.
- Gradient Descent Optimization: In…
