What are the common mistakes in data scaling?

What are the common mistakes in data scaling?

Common mistakes in data scaling include not normalizing data, ignoring outliers, and using inappropriate scaling methods.

Can you explain why normalizing data is important?

Normalizing data ensures that each feature contributes equally to the distance calculations, improving model performance.

What are some appropriate scaling methods?

Some appropriate scaling methods include Min-Max scaling, Standardization (Z-score), and Robust scaling.

How do outliers affect data scaling?

Outliers can skew the scaling process, leading to distorted results and negatively impacting model accuracy.

What should I do if I have many outliers?

Consider using Robust scaling or transforming the data to reduce the influence of outliers.