What are some effective scaling techniques in machine learning?

What are some effective scaling techniques in machine learning?

Some effective scaling techniques include normalization, standardization, and min-max scaling. Would you like to know more about any specific technique?

Can you explain normalization and when to use it?

Normalization rescales the data to a range of [0, 1]. It's useful when you want to ensure that all features contribute equally to the distance calculations.

What about standardization? How is it different?

Standardization transforms data to have a mean of 0 and a standard deviation of 1. It's particularly useful for algorithms that assume a Gaussian distribution.

Are there any techniques for scaling large datasets?

Yes, techniques like distributed computing, batch processing, and using libraries like Dask or Apache Spark can help scale large datasets effectively.