Steve knows what he is talking about here.
The above demonstration with JASP was done in about an hour with the time mostly spent on writing and screen shots. And a little time with JASP.
Normally one would spend some time adjusting (and validating) the hyper parameters in a Boosting program.
The only hyper parameter I changed was “Shrinkage” to 0.01 (based on previous experience with boosting programs). I also changed the Training and Validation Data to K-fold with 5 folds which is not a hyper paramater. That was all the time that I spent. I did this before I saw how the test sample performed.
I thought my point was already made. And that no one would claim that changing these 2 things from their default settings was just too hard for a serious investor.
Anyone wanting to spend more time with JASP should also change “Min. observations in node” and “Interaction depth.” The defaults that I used here are almost certainly not optimal. And the optimal hyper parameters will be different for different data (including your data).
The real time that I have spend with boosting has been with XGBoost which is the program professionals use and it does offer some additional capabilities. But is XGBoost better than a neural net as Steve says?
Steve’s opinion of neural nets is shared by many. Here is a quote from the web. I do not think it is from a famous person but the same quote can be found everywhere:
“Xgboost is good for tabular data………whereas neural nets based deep learning is good for images….”
“Tabular data” is just what is found in an Excel spreadsheet.
I actually disagree with this blanket statement. TensorFlow can beat XGBoost SOMETIMES, I think.
But XGBoost is the place to start. And Steve is using TensorFlow too.
If you just want to make money you should see if Steve has something you can use.