To understand the origin of this effect, we recall how ∆SOC PE,2.7V and ∆SOC NE,2.7V are calculated. These quantities are calculated at a specified full cell voltage (2.7V for this example) and, as a result, are highly correlated (Fig. , SI Section S.8.1). This correlation arises because when one electrode's SOC shifts, regardless of the aging mechanism, the other electrode's SOC must shift in the opposite direction to produce the same measured full cell voltage (SI Fig. explores this in further detail). In general, SHAP is unable to differentiate between highly correlated features, and repeating the SHAP analysis multiple times reveals that either ∆SOC PE,2.7V or ∆SOC NE,2.7V can emerge as the most dominant feature (SI Fig. ). However, if ∆SOC NE,2.7V is removed from this explanatory model, for example, ∆SOC PE,2.7V appears as the dominant feature (SI Fig. ). From this analysis, we understand that, while negative electrode over-discharging (∆SOC NE,2.7V < 0) leads to lower resistance increase, the correlated metric positive electrode over-discharging (∆SOC PE,2.7V < 0) leads to higher resistance increase, in line with the understanding that electrode kinetics are most sluggish at SOC extremes. Combining statistical analysis with scientific understanding of battery materials, we rationalize that low SOC resistance rise is dominated by the over-discharging of the positive electrode.