Algorithm is the combination of sequential growth by combining all the previous iterations in the decision trees. Overfitting is reduced with the help of regularization parameters in XGBoost that helps to select features based on weak and strong features in the decision tree. Only a random subset of features is selected always that are included in the decision tree so that the result is not dependent on any subset of data. Random subsamples of data are selected for Random Forest where the growing happens in parallel and overfitting is reduced with the combination of several underfitting features in the algorithm.Either random subset of features or bootstrap samples of data is taken for each experiment in the data. Random Forest is mostly a bagging technique where various subsets are considered and an average of each subset is calculated. Sample is not modified here but different levels of importance are given to each feature in the data. XGBoost helps in numerical optimization where the loss function of the data is minimized with the help of weak learners so that iteration happens in the local function in a differentiable manner.Also, we can take samples of data if the training data is huge and if the data is very less, we can use the entire training data to know the gradient of the same. We can use XGBoost to train the Random Forest algorithm if it has high gradient data or we can use Random Forest algorithm to train XGBoost for its specific decision trees. The training methods used by both algorithms is different. XGBoost trains specifically the gradient boost data and gradient boost decision trees. Random Forest and XGBoost are decision tree algorithms where the training data is taken in a different manner.Let us discuss some of the major key differences between Random Forest vs XGBoost: For data including categorical variables with different number of levels, random forests are biased in favor of those attributes with more levels.Key Difference Between Random Forest vs XGBoost The main limitation of the RF algorithm is that a large number of trees can make the algorithm slow for real-time prediction. In RF we have two main parameters: number of features to be selected at each node and number of decision trees. The model tuning in RF is much easier than in case of XGBoost. Our data set is very noisy and contains a lot of missing values e.g., some of the attributes are categorical or semi-continuous. Our goal is to have high predictive accuracy for a high-dimensional problem with strongly correlated features. RF model is very attractive for this kind of applications in the following two cases: to find clusters of patients based on tissue marker data. The random forest dissimilarity has been used in a variety of applications, e.g. Thanks to that RF is less likely to overfit on the training data. This randomness helps to make the model more robust than a single decision tree. Random Forest (RF) trains each tree independently, using a random sample of the data. There are typically three parameters: number of trees, depth of trees and learning rate, and the each tree built is generally shallow. Training generally takes longer because of the fact that trees are built sequentially. XGB model is more sensitive to overfitting if the data is noisy. This including things like ranking and poisson regression, which RF is harder to achieve. Since boosted trees are derived by optimizing an objective function, basically XGB can be used to solve almost all objective function that we can write gradient out. Examples of such data sets are user/consumer transactions, energy consumption or user behaviour in mobile app. In this case XGB is very helpful because data sets are often highly imbalanced. We use XGB models to solve anomaly detection problems e.g.

Each new tree corrects errors which were made by previously trained decision tree. XGBoost build decision tree one each time. XGBoost (XGB) and Random Forest (RF) both are ensemble learning methods and predict (classification or regression) by combining the outputs from individual decision trees (we assume tree-based XGB or RF).

Xgboost vs random forest