Target Variable

The target variable is the feature of a dataset that you want to understand more clearly. It is the variable that the user would want to predict using the rest of the dataset. In most situations, a supervised machine learning algorithm is used to derive the target variable. Such an algorithm uses historical data to learn patterns and uncover relationships between other parts of your dataset and the target. Target variables may vary depending on the goal and available data.

Why are Target Variables Important?

In the absence of a labeled target, supervised machine learning algorithms would not be able to map available data to outcomes.

• Example: A child would be incapable of figuring out that dogs are called dogs without being told a few times. Well-defined targets are important, as the only thing the algorithm does is learn a function that maps the relationship between input data and the target. The model’s outcomes mean nothing if the target isn’t well understood.

How Do I Determine the Target Variable?

Determining the target variable can require running an existing suboptimal system until enough training data is collected.

• Example: When building a machine learning solution for measuring customer attrition in the telecommunication industry, you need to spend significant time observing, weeks or even months, as some customers unsubscribe and others renew. Once you have enough training instances to build an accurate machine learning model, you can begin using machine learning in production.

Target Variables vs Other Technologies & Methodologies

Target variable vs predictor variable

The target variable is the variable whose values are modeled and predicted by other variables. A predictor variable is a variable whose values will be used to predict the value of the target variable.

Target Variable in H2O AI

In H2O-3 you name the target with the y parameter. In H2O Driverless AI, our AutoML, you choose it as the Target Column of an experiment.