Ensemble model combines multiple 'individual' (diverse) models together and delivers superior prediction power. ... A good example of how ensemble methods are commonly used to solve data science problems is the random forest algorithm (having multiple CART models).

Dear Elahe Abbasi,

Related to your query, I suggest you follow https://www.rulequest.com/cubist-unix.html

https://topepo.github.io/Cubist/reference/cubist.default.html

thanks @Muhammad Ali

The Cubist model is a rule–based model.

https://www.rulequest.com/cubist-info.html

It is an extension of Quinlan’s M5 model tree.

The model itself is an individual model but it can also be used as boosting-like ensemble as you can read it here: https://mran.microsoft.com/snapshot/2016-09-15/web/packages/Cubist/vignettes/cubist.pdf

So the Cubist in R supports ensemble usage of the model.