I am writing to seek clarification regarding the temporal boundary of the features provided in the training and test datasets. The competition objective is to predict the probability of adoption within 7 days of a farmer's first training, using information available at the time of training.

Upon reviewing the data dictionary, I am concerned that several features may incorporate information from a future window (data occurring after the 7-day target period). If these features were calculated at the end of the data collection period rather than at the 7-day cutoff, they may constitute Target Leakage, allowing models to 'cheat' by observing post-event behavior.

Could you please confirm if the following features are strictly constrained to observations made on or before the 7th day, or if they include data from the full 30, 60, or total duration of the study?

Features in Question:

num_trainings_30d: Does this count trainings that occurred between Day 8 and Day 30?num_trainings_60d: Does this count trainings that occurred between Day 8 and Day 60?num_total_trainings: Does this include all sessions regardless of the date? num_repeat_trainings: Does this count sessions attended after the initial 7-day adoption window? num_unique_trainers: Was this calculated based on trainers met after the adoption window? days_to_second_training: If the second training occurred on Day 10, is this value available to the model, despite being 'future' information relative to the Day 7 target?

If these features contain information from after the 7-day window, they would not be available in a real-world deployment scenario. Clarification on whether we are permitted to use these for the final submission or if they should be treated as leakage would be greatly appreciated.