At this stage, a list of numbers representing the distance of the target task from all the source tasks has been generated for each target task. However, we aim to assign a single number to each target task that quantifies its difficulty, considering all the source tasks from a chemical space perspective. The intuition is that, after applying meta-learning, we can also derive a single number for each task indicating the improvement or decline in prediction performance, leveraging all the information from the source. Various aggregation functions could be employed to distill the list into a single number. We opted for the average of the k-nearest dataset distance as our aggregation method. We consider the k closest source tasks for a given target task and average their distances to calculate the task hardness. This process is repeated for all target tasks. If k source tasks are close to the test task (based on the defined metric), the hardness would be small, and the task would be considered easy. The opposite is true when the task is considered hard. Ultimately, we normalize these hardness numbers using the Min-Max normalization schema.