Both Random Forest (RF) and Gradient Boosting (GBM) are tree-based supervised machine learning algorithms. Both use a tree-based modeling approach and ensemble methods are used. RF uses decision trees, kind of complex form of a tree-based algorithm, which is inclined to overfitting. GBM instead is a boosting-based algorithm approach, which is based on weak classifiers. Accuracy of RF can be manipulated by modifying variance. GBM will have more hyper-parameters to tune for accuracy and can be planned to play for a tradeoff between bias and variance.