  The effective charge of an element is a parameter characterizing the
electromgration effect, which can determine the reliability of interconnection
in electronic technologies. In this work, machine learning approaches were
employed to model the effective charge (z*) as a linear function of physically
meaningful elemental properties. Average 5-fold (leave-out-alloy-group)
cross-validation yielded root-mean-square-error divided by whole data set
standard deviation (RMSE/$\sigma$) values of 0.37 $\pm$ 0.01 (0.22 $\pm$ 0.18),
respectively, and $R^2$ values of 0.86. Extrapolation to z* of totally new
alloys showed limited but potentially useful predictive ability. The model was
used in predicting z* for technologically relevant host-impurity pairs.
