I need to train a supervised learning model which would take some input which differs in its output relating to time. to better understand my question I would give a simple binary classification, the model would receive an object that changes according to time so on time period t0 the output of the model related to that input differs from the output of the time period t1. I might be describing something either very easy and I missed it or something that doesn't yet exist. EDIT: I am preparing a model that classifies fruit based on its appearance to either be consumable or not. The only different thing in my work is that in specific time periods what was considered not consumable should be considered consumable so a picture of a fruit in a certain state could be classified both ways in general but in a specific time period it has only one classification.
Supervised learning models can handle time-varying data by using a variety of techniques. One common approach is to use a Sliding Window, where the model is trained on data from a fixed period and then applied to data from a different period. Another approach is to use a Recurrent Neural Network, which can learn temporal dependencies in data. If the input data changes over time, the model can learn to adapt and update its predictions accordingly.
It sounds like you are looking for a model that can learn to classify the fruit based on its appearance, and then can also learn to update its classification based on the specific period. This type of model does not currently exist, but it could be developed with enough data and training.You will need to design your own AI algorithm to accomplish this task.