PURPOSE: Speech understanding in noise can be effortful, especially for people with hearing impairment. To compensate for reduced acuity, hearing-impaired (HI) listeners may be allocating listening effort differently than normal-hearing (NH) peers. We expected that this might influence measures derived from the pupil dilation response. To investigate this in more detail, we assessed the sensitivity of pupil measures to hearing-related changes in effort allocation. We used a machine learning-based classification framework capable of combining and ranking measures to examine hearing-related, stimulus-related (signal-to-noise ratio [SNR]), and task response-related changes in pupil measures. METHOD: Pupil data from 32 NH (40-70 years old, RESULTS: A combination of pupil measures was necessary to classify the dependent factors. Hearing status, SNR, and task response were predicted primarily by the established measures-PPD (maximum effort), RPC2 (speech processing), and BPS (task anticipation)-and by the novel measures RPC1 (listening) and RPC3 (response preparation) in tasks involving SNR as an outcome or sometimes difficulty criterion. CONCLUSIONS: A machine learning-based classification framework can assess sensitivity of, and rank the importance of, pupil measures in relation to three effort modulators (factors) during speech perception in noise. This indicates that the effects of these factors on the pupil measures allow for reasonable classification performance. Moreover, the varying contributions of each measure to the classification models suggest they are not equally affected by these factors. Thus, this study enhances our understanding of pupil responses and their sensitivity to relevant factors. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.28225199.