Thank you for providing further Feature Selection workflows.
I have 100 Features and approximately 750 rows (I cant influence that, this is the actual data I received). Basically, all features tell me if a customer buys a product or not - and all these Features are numeric. At the end there is the Target Variable (Boolean 1,0), 1 - says yes, the customer buys, and 0 - is no. So - this is a classical example of Supervised Learning.
So, I guess then, in my case it only makes sense to select features based on correlation (meaning delete highly correlated features) and then simply put them into Random Forest Learner.
Would you see it as a good solution? And would your Workflow (the one you posted above with H2O) is a good suitable example for that?
Thank oyu,
Kind Regards,
Alina