I have a dataset like below without labels
But with the help of experts opinion, we generate labels based on the below 3 rules (all 3 rules has to be met to label it as 1)
So now the dataset looks like below
As you can see that my final dataset has the labels.
Now I can run a ML model for classification. Am I right?
But I read that during model building process, features that were used to create the labels will have to be excluded because they might result in perfect separation of classes and model might fail. what does it mean by fail? Aren't we aiming for separation of classes through classification algorithms?
May I know why do we have to exclude these features (Ex: RG, FG and BP features which were used to derive labels)?
It's basically my model will be built on below dataset. But aren't we losing the predictive power? why do we have to build a model by excluding those features (that were used to derive labels)?