In this perspective, we analyze the Severson dataset to demonstrate that the community needs to pay attention to which features to use, depending on the usage scenario. Using features developed by Greenbank and Howey extracted on (1) the charging data, (2) the discharging data, and (3) the entire charge-discharge data, we show that models relying on features based on the charging data have substantially better prediction accuracies than models using features from the discharging data. This is because these former models directly capture protocol-to-protocol variations. In fact, we show that models using no aging data at all and only the cycling conditions give good performance. However, when deployed on identically cycled cells, models based on the charging data do not maintain the same level of prediction accuracy. More generally, we show that feeding information about the aging conditions into a lifetime prediction ML model will bias the model to learn the protocol-to-protocol variations instead of learning the cell-to-cell variations. Thus, for use cases aiming to detect cell-to-cell variability, models need to be blinded from cycling conditions to avoid information leakage.