Introduction to Boosting Algorithms

   Boosting is one of the ensemble learning methods in machine learning to improve model prediction. The main concept of boosting is to improve (boost) the week learners sequentially and create a combined and more accurate model. There are several boosting algorithms such as Gradient Boosting, AdaBoost, XGBoost, and others. We can apply a boosting technique to build a better model in regression and classification problems in machine learning.

Key Concepts in Boosting

   Here, I'll mention some of the main concepts used in boosting to understand the algorithm well.  You can find a lot of web resources that explain each topic thoroughly.

The decision tree algorithm is a tree-like structure with certain decisions to predict target data. To learn decision rules, train data is split into subsets, and each subset should contain a common attribute value. The decision tree algorithm often used as a base method in boosting.

Overfitting means that the model fits too well. It happens when the model goes deeper and deeper to learn the details of the training data. Eventually, the process negatively impacts the model performance.

A weak learner is defined as the one with poor performance or slightly better than a random guess.

Regularization is a technique to reduce overfitting. Fitting the train data too close decreases the model's generalization capability. One of the regularization methods is to consider the iteration number in the training process.

Boosting Examples with R

   Boosting examples for the classification problem in R is explained in the below. You may check the details of each method in the below links.

   In R, there are several packages to implement boosting algorithms. An 'adabag' package provides a 'boosting' function to apply the adaboost method. In my test case, its performance was comparatively slower than the other two methods that are xgboost and gradient boosting. The 'xgboost' package's xgboost function performed fast in the classification test. Since my test data is simulated and straightforward (for learning purpose), all models performed well and accurately. To know the actual capability of each model we need to check them with larger datasets.

   Thank you for reading!

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