IDK Cascades are a framework used to improve time usage within classification tasks. A cascade is comprised of a series of classification models with increasing levels of complexity. A prediction occurs when a single model's computed confidence is above a certain threshold; otherwise, an IDK classification is made, which leads to the next model in the series considering the input. A recent improvement to this scheme is the idea of skipping a model in the case where the prior model's confidence was extremely low. However, this method has inherent issues when predicting whether to skip. This paper improves on the method of skipping certain models by using a Random Forest model trained on multiple parameters, resulting in smart skipping. Unlike the previous threshold skipping approach, the Random Forest's is able to more accurately predict when a skip should occur, leading to larger time savings. Ultimately, we are able to obtain an IDK Cascade utilizing Random Forest skipping that is more computationally efficient than threshold skipping across three datasets.