Feature Scaling for Machine Learning Models: 90 Basic — Advanced Tips and Strategies

Photo by Jean-Philippe Delberghe on Unsplash

Feature scaling is an important preprocessing step in machine learning that helps ensure that features are on a similar scale, preventing certain features from dominating others. Here are 90 tips and tricks for feature scaling:

1. Basics of Feature Scaling

1. Understand the importance of feature scaling in machine learning.
2. Be aware of algorithms sensitive to feature scales, such as k-nearest neighbors or support vector machines.
3. Normalize features to a similar range for better convergence in gradient-based optimization algorithms.
4. Standardize features to give them zero mean and unit variance.
5. Apply feature scaling to numerical features, but not typically to categorical or binary features.
6. Consider the distribution of your data when choosing a feature scaling method.
7. Implement feature scaling consistently across training, validation, and test sets.
8. Regularly visualize and analyze the distribution of scaled features.
9. Document the feature scaling method used for reproducibility.
10. Monitor the impact of feature scaling on model…