AutoML, or Automated Machine Learning, efficiently manages categorical data through several methods that simplify the preprocessing and modeling stages. Categorical data refers to variables that represent distinct categories, such as "color" (e.g., red, blue, green) or "city" (e.g., New York, Los Angeles). Since many machine learning algorithms cannot directly work with this type of data, AutoML solutions apply techniques like encoding to transform these categories into a numerical format that models can interpret.
One common method used by AutoML for handling categorical data is one-hot encoding. This technique creates binary columns for each category in a categorical feature. For instance, if we have a categorical variable for "fruit" with three categories: apple, banana, and cherry, one-hot encoding will generate three new columns. Each row in the dataset will have a 1 in the column that corresponds to the category present and 0s in the others. This allows machine learning algorithms to have a clear and direct way to interpret the categorical variables without introducing a misleading ordinal relationship.
Another approach is label encoding, where each category is assigned a unique integer value. For example, apple might be encoded as 0, banana as 1, and cherry as 2. This method is simpler but can sometimes lead to problems, as the algorithm might misinterpret the numerical values as ordinal data. AutoML platforms often include options for both one-hot and label encoding and may automatically choose the best strategy based on the specific dataset and algorithm being used. Additionally, some advanced AutoML tools can handle high-cardinality categorical data through techniques like target encoding, which replaces categories with the mean of the target variable for those categories, further enhancing model performance.