As we've seen in bagging, we create a number of bags on which each model is trained. Each of the bags consists of subsets of the actual dataset, however the number of features or variables remain the same in each of the bags. In other words, what we performed in bagging is subsetting the dataset rows.
In random forests, while we create bags from the dataset through subsetting the rows, we also subset the features (columns) that need to be included in each of the bags.
Assume that you have 1,000 observations with 20 features in your dataset. We can create 20 bags where each one of the bags has 100 observations (this is possible because of bootstrapping with replacement) and five features. Now 20 models are trained where each model gets to see only the bag it is assigned with. The final prediction is arrived at by voting or averaging...
