Cosmetics legislation in Europe has driven the validation and acceptance of non-animal alternatives, most recently in the area of skin sensitization. Despite use of these methods to meet regulatory needs, it is also essential that they allow evaluation regarding human safety. For cosmetic product safety, it is necessary to understand how they can be used and with what limitations, and thereby reveal what remains to be addressed. A dataset of 165 ingredients (137 cosmetic ingredients +28 reference substances) has been identified, curated, and subjected to testing using accepted in vitro methods, with additional information, including physicochemical data and in silico results. The inputs from multiple determinants of skin sensitizing activity have been used in five individual supervised classification models (or machine learning approaches), which were then collated in a robust statistical manner, a stacking meta-model, to deliver a prediction with an optimized level of confidence. For the training set, with the probability cutoffs at 70% and 30%, predictive sensitivity was 97%, specificity was 88%, the overall accuracy was 93%, and kappa was 85%. A further 52 substances were used to test the effectiveness of the model: the predictive sensitivity was 89%, specificity was 95%, overall accuracy was 91%, and kappa was 82%. In conclusion, this stacking meta-model delivers improved performance and therefore enhanced confidence in the discrimination of skin sensitizers from nonsensitizers. The key remaining gap, prediction of skin sensitization potency, may benefit from a similar approach, maximizing use of evidence from individual strands of prediction, while minimizing the impact of the limitations from any particular one.