Learning curves represent the relationship between the amount of training data and the error rate in machine learning. An important use case for learning curves is extrapolating them in order to predict how much data is needed to achieve a certain performance. One way to do such extrapolations is using Deep Learning with a Prior-Fitted Network(PFN). This paper explores how training the PFN on an imbalanced dataset, i.e. containing learning curves from two or more machine learning models with a skewed distribution, affects the performance of the network. Research into imbalanced learning has shown that machine learning models can favor the more prevalent classes or data. Therefore, it is worthwhile to explore whether such trends can occur for the neural networks that we train for learning curve extrapolation. Our experiments focused on analyzing different imbalance scenarios and comparing them. Our results show that mixing learning curves from different learners can improve extrapolation performance in some cases, but the effect strongly depends on the learner characteristics and training proportions.