  In this study, we demonstrate how the incorporation of appropriate feature
engineering together with the selection of a Machine Learning (ML) algorithm
that best suits the available dataset, leads to the development of a predictive
model for transformation temperatures that can be applied to a wide range of
shape memory alloys. We develop a gradient boosting ML surrogate model capable
of predicting Martensite Start, Martensite Finish, Austenite Start, and
Austenite Finish transformation temperatures with an average accuracy of more
than 95% by explicitly taking care of potential distribution changes when
modeling different alloy systems. We included heat treatment, rolling,
extrusion processing parameters, and alloy system categorical features in the
model input features to achieve more accurate and realistic results. In
addition, using Shapley values, which are calculated based on the average
marginal contribution of features to all possible coalitions, this study was
able to gain insights into the governing features and their effect on predicted
transformation temperatures, providing a unique opportunity to examine the
critical parameters and features in martensite transformation temperatures.
