What is label encoding used for?

Label encoding is a technique primarily used in the context of preparing categorical data for machine learning algorithms. This method involves converting categorical values into a numerical format, which is essential since many machine learning algorithms require numerical input to perform calculations and make predictions.

When categorical variables are converted to numeric codes through label encoding, each unique category is assigned a unique integer. For example, consider a categorical variable "Color" with values "Red," "Green," and "Blue." Label encoding will convert these categories to integers like 0, 1, and 2, respectively.

This transformation allows machine learning models to process categorical data effectively, enabling them to learn from it during training. Thus, label encoding acts as a bridge between categorical features and numerical computations required by various algorithms, enhancing model performance and accuracy.

In contrast, other options provided do not capture the primary use of label encoding accurately. For instance, converting numerical data into categorical format, visualizing relationships in data, and categorizing numerical data do not align with the fundamental purpose of label encoding.