In machine learning, what is the variable that you are attempting to predict in a training set called?

In machine learning, the variable that you are attempting to predict in a training set is referred to as a "label." The label represents the outcome or target that the model is trying to learn to predict based on the associated input data.

When building a supervised learning model, for example, the training data consists of input features and their corresponding labels. The model uses these pairs to learn the relationship between the features and the labels, enabling it to make predictions on unseen data.

While terms like "categories," "features," and "attributes" are frequently used in the context of machine learning, they have specific meanings that differentiate them from labels. Features are the input variables used to make predictions, categories may refer to the distinct classes in classification problems, and attributes are often synonymous with features, especially in contexts involving databases or data structures. However, labels specifically denote the outcomes we aim to predict, making it the correct term in this case.