What are normalization techniques in data processing?

What are normalization techniques in data processing?

Normalization techniques are methods used to adjust the values in a dataset to a common scale, improving data analysis.

Why is normalization important in machine learning?

Normalization helps in speeding up the training process and improving the performance of machine learning models by ensuring that features contribute equally.

Can you explain different types of normalization techniques?

Sure! Common normalization techniques include Min-Max scaling, Z-score normalization, and Decimal scaling.

When should I use Min-Max scaling over Z-score normalization?

Use Min-Max scaling when you want to bound your data within a specific range, while Z-score normalization is better for data with outliers.