There are a few simple tests that you can do that are useful in similar situations. First make really sure that you haven’t somehow blended the dominant predictor into your response variable. It’s more common the other way around, where people create a predictor variable that, by mistake, uses the response variable and then, voilà, GeneXproTools finds a perfect solution (happens to the best of us, I’m afraid :). But since in this case the culprit (PC_RSI) is something external, please make sure that your response is not using it in some indirect way.

Just by the analysis of the correlations of each predictor variable against the response (which you can do in the Data Panel), you can see that there’s nothing overtly suspicious there, although it has by far the highest R-square (0.1785; the second best is around 0.009).

Another neat and simple thing to do is to use the new feature Add Simple Models, and add your predictor variables to the History. When you do that, GeneXproTools evaluates the rounding thresholds automatically for these simple models. In this case the PC_RSI simple model has already 96.55% and 98.74% accuracy in the training and validation, respectively.

And another thing you can do is to create a strictly linear logistic regression model with just this variable. You can do that in GeneXproTools easily by creating a seed model with a simple structure with a head size of 3 and as many genes as you have variables. Since in this case you’re only concerned with one variable, you just need 1 gene and a head such as *.c0.d0 where d0 is your variable. Then, in the Genetic Operators Tab, choose the strategy Constant Fine-Tuning, and then do Continue in the Run Panel. If you do that, you’ll see that this linear LR model has the same performance as the simple model mentioned above.

And yet another thing you can do is create a more sophisticated model using just the simple functions + - * and a simple program architecture and then simplifying it in the end. In this case it was also possible to create very good models with this simple architecture. For example, I created a model with 99.37% accuracy in the training and 100% in the validation.

I hope this helps in pointing you in the right direction.

Candida