What do you understand by a random forest model?
Let me try to answer it in an over simplified way
Lets say if you were to make a prediction either binary or multi class or a numeric value. You would build a logical equation from data . Lets say we want to predict house of an apartment in mumbai - you would say if the area is 1000 sq.ft, if the age of the building is 7 years etc then the price will be X. similarly for a binary prediction you will say with the above attributes, the apartment is highly likely to sell soon or not. Now for a different set of attributes, you will get a different answer. Now image this list of attributes in he form of a tree - If the sq ft is below 1000, if the age is above 5 years then a particular answer. These are called decision tree models in data science. Random forest is the advanced machine learning version of decision trees where in order to get better accuracy , it creates multiple trees on various samples from the same data and aggregates them at the end to give an answer which makes it more accurate that a simple single decision tree.