What is categorical data encoding and why is it important?

What is categorical data encoding and why is it important?

Categorical data encoding is the process of converting categorical variables into a numerical format that machine learning algorithms can understand. It's important because many algorithms require numerical input to perform calculations.

What are some common techniques for encoding categorical data?

Some common techniques include One-Hot Encoding, Label Encoding, and Binary Encoding. Each has its own use cases depending on the data and the model.

When should I use One-Hot Encoding over Label Encoding?

Use One-Hot Encoding when the categorical variable is nominal and does not have an ordinal relationship. Label Encoding is suitable for ordinal variables where the order matters.

Can you give an example of One-Hot Encoding?

Sure! If you have a categorical variable 'Color' with values 'Red', 'Green', and 'Blue', One-Hot Encoding would create three new binary columns: 'Color_Red', 'Color_Green', and 'Color_Blue', where each row has a 1 or 0 indicating the presence of that color.