Table of Contents
Feature scaling is an important preprocessing step in many machine learning algorithms, especially in unsupervised learning. Proper scaling ensures that all features contribute equally to the analysis and improves the performance of algorithms such as clustering and dimensionality reduction.
Why Feature Scaling Matters
In unsupervised learning, algorithms often rely on distance measurements or similarity metrics. If features are on different scales, features with larger ranges can dominate these calculations, leading to biased results. Scaling helps to normalize feature contributions and enhances the accuracy of the models.
Common Scaling Techniques
- Min-Max Scaling: Transforms features to a fixed range, usually [0, 1].
- Standardization: Centers features around the mean with a standard deviation of 1.
- Robust Scaling: Uses median and interquartile range to reduce the influence of outliers.
Best Practices for Scaling
Apply scaling techniques after splitting data into training and testing sets to prevent data leakage. Fit the scaler on the training data only, then transform both training and testing data. This approach maintains the integrity of the evaluation process.