Bifalgorithm post

What is the difference between XGBoost and Random Forest? So, in simple words, both XGBoost and Random Forest build multiple decision trees to make predictions, but the way they build those trees is completely different. Now let's understand each one. Random Forest builds all its trees independently and in parallel. Each tree is trained on a random subset of the data and a random subset of the features. and at the end all the trees vote together and the majority answer wins. Whereas XGBoost builds its trees sequentially, one after another. The first tree makes a prediction, then XGBoost looks at where that tree went wrong and builds a second tree specifically to fix those mistakes, then a third tree to fix the remaining mistakes, and so on. So each tree learns from the errors of the previous one, and this technique is called Gradient Boosting. Now which one should you use? So, Random Forest is faster to train, easier to tune and less likely to overfit. So, it is great when you need something reliable and quick, whereas XGBoost is slower but almost always more accurate, especially on structured tabular data and that's why it dominated Kaggle

What is the difference between XGBoost and Random Forest? So, in simple words, both XGBoost and Random Forest build multiple decision trees to make predictions…