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Xgboost vs random forest1/17/2024 ![]() The algorithm is computationally more efficient due to the inclusion of the second order derivative in its search strategy, but also less prone to overfitting due to the addition of a regularization term (similar to regression, or cost-complexity in decision trees). XGBoost algorithm is an improved implementation of the gradient boosting algorithm. The original gradient boosting algorithm is implemented in the gbm package, however nowadays another boosting variant has taken over, namely XGBoost. This causes both the step size of the gradient descent algorithm and the used loss function to be important paramaters. More importantly, since gradient boosting can be solved by using gradient descent, it can optimize different loss functions. Rather than simply weighting instances based on the misclassification costs, other step sizes are possible. While AdaBoost is a special case with a particular loss function, gradient boosting can be regarded as an ‘overall’ solution to building sequential learners. Gradient boosting is a generalization of adaboost. This caused them to come up with gradient boosting. Researchers were attracted to the sequential learning idea behind adaboost. You can try for yourself what optimizing the mtry would do. Note that random forest can be sensitive to the mtry parameter, or the number of random variables to select at each split. Almost all modelers add a random forest model to compare their performance with. By the way, it is exactly this performant, robust behaviour which makes random forest such a popular algorithm. ![]() Just set you number of trees large enough. We already observe a very flat out-of-bag error.However, when we select the optimal number of trees according to this plot, we see that performance is much lower than when we did not optimize for the value. This is # because there are much more zeroes to learn from. RFmodel <- randomForest( x = BasetableTRAIN, y = yTRAIN, xtest = BasetableVAL, ytest = yVAL, ntree = 1000, importance = TRUE) # Make a plot red dashed line: class error 0 green dotted # line: class error 1 black solid line: OOB error we see # that class 0 has lower error than class 1. ![]() We will be using again the data from our NPC. Now let us put this to practice ourselves. Aspects that are present in each tree will weigh more heavy in the overall predictions. Only the tree structures that are present in most trees will be representive of the true underlying relationship (without overfitting)! By aggregating (averaging) all the predictions, you have predictions which incorporate the aspects of each unique tree. These small deviations cause the trees to become relatively different. Bagging is short for bootstrap aggregation and a bootstrap sample is nothing different than a sample with replacement.Īs you take different samples for each tree, small deviations occur in the training set. This knowledge is used in bagged decision trees. This means that small deviations in the training data are enough to deliver very different decision trees (a small deviation in the top node split will heavily impact the rest of the tree’s structure). This is because decision trees tend to overfit heavy to the data and are unstable. A popular building block for ensembles are decision trees. While you can build any combination of single classifiers by use of a certain aggregation rule (e.g., performance-weighted predictions), several popular pre-configured ensembles exist. Such a combination of single classifiers is called an ensemble. To overcome this, people thought about combining several single classifiers. This results in several model-specific issues: a regression model never learns complex nonlinear relations, and a decision tree will never know how to estimate the impact of an increase beyond the observed training range (as no splits can be made there, notice the difference with the ever-inclining regression slopes). Those algorithms all try to match a pre-specified model structure to the relationship between predictors and response. Up until now we have seen single classifiers. ![]()
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