Subject: Re: ML for MI
QUOTE : In my models I trained on different 5 year periods of data and tested on following 2 years after a gap to prevent data leakage from data they had not seen. over 100’s of times they have in over 85% of the time performed well on the unseen data and never worse than average. And no I have never had to retrain them.
ENDQUOTE

I of course do not know your objective ie success parameters of your model - so this step is absolutely sensible but not enough. You need to ensure that you separately evaluate performance at points of inflection. Some are obvious - but in the post 2009+ Mega Bull ..... your holdout/validation years need to have 2011,2016,2018,2020 and 2022 separated out. If something works well treating these as unseen ie using data before these years and validated on these post - and of course large scale would be 1990s validated on 2000-2002 and pre-2008 validated 2008-09 - then you will have confidence in your models

Best