What is feature scaling and how do outliers affect it?

What is feature scaling and how do outliers affect it?

Feature scaling is the process of normalizing or standardizing the range of independent variables in data. Outliers can significantly skew the results of scaling, leading to misleading interpretations.

Can you explain the different methods of feature scaling?

Sure! The two most common methods are Min-Max scaling, which rescales the data to a fixed range, usually [0, 1], and Standardization, which centers the data around the mean with a unit standard deviation.

How do I handle outliers before scaling?

You can handle outliers by removing them, transforming the data (like using log transformation), or using robust scaling methods that are less sensitive to outliers.

What are some examples of robust scaling methods?

Examples include the Robust Scaler, which uses the median and interquartile range, and the use of Z-scores based on median and MAD (Median Absolute Deviation).