There is a growing demand for high-reliability and high-quality integrated circuit (IC) products, while their test costs should be kept as low as possible. We investigate the test process of advanced memory chips, where the high temperature operating life (HTOL) test has been used to determine their intrinsic reliability. This high temperature sampling test can run from 168 to 1,000 hours, so it is time-consuming and expensive. Recently, machine learning (ML) algorithms have been used to solve classification problems, so far as good training data can be obtained. In our case, there is already a large amount of parametric test data generated from the existing test flow. Therefore, in this work, we propose a decision tree (DT)-based screening method to predict weak (unreliable) dies that would fail the HTOL test. We show that experienced test engineers can prioritize the parametric test data for better use of the DT model. Finally, we take advantage of the high interpretability of DT to develop the multi-feature heuristics, which can be used to improve the quality of final test (FT). Keeping the overkill rate at 0%, our heuristics can screen out 25% more bad dies, i.e., we can improve the FT quality without additional cost.