The main difference between a random forest and GBM is the use of techniques. Random forest advances predictions using a technique called ‘bagging.’ On the other hand, GBM advances predictions with the help of a technique called ‘boosting.’
- Bagging: In bagging, we apply arbitrary sampling and we divide the dataset into N After that, we build a model by employing a single training algorithm. Following, we combine the final predictions by polling. Bagging helps increase the efficiency of the model by decreasing the variance to eschew overfitting.
- Boosting: In boosting, the algorithm tries to review and correct the inadmissible predictions at the initial iteration. After that, the algorithm’s sequence of iterations for correction continues until we get the desired prediction. Boosting assists in reducing bias and variance, both, for making the weak learners strong.