What is a 'leaker' in Einstein Prediction Builder?

In the context of Einstein Prediction Builder, a 'leaker' refers to a field that is populated with data after the outcome of the prediction is already known. This can skew the results of a predictive model because it provides information that would not have been available at the time the prediction was made. For example, if you were trying to predict whether a customer would churn and included a field indicating whether the customer actually churned, the model would have access to future information, making the prediction unreliable. By identifying ‘leakers’, data scientists and analysts can refine their models to ensure they accurately reflect true predictive capabilities without including biased information. This clarity in the data allows for more reliable and actionable insights from the predictions generated by Einstein.

The other options, while relevant to predictive modeling or data management in general, do not specifically define what a 'leaker' is within the context of Einstein Prediction Builder.