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 model too much decreases the model's generalization capability and creates an overfitting issue. Regularization help to keep the balance between the overfitting and underfitting. 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. Please check the links to learn more about them.

    Classification with AdaBag Boosting in R. Here, we use the Adabag package's booting method.

   Gradient Boosting Classification with GBM in R. We use the Gbm packages to classify the Iris dataset in this tutorial.

   Classification with XGBoost Model in R. The Xgboost package's Xgboost method to classify the Iris dataset was explained.

   In R, there are several packages to implement boosting algorithms. The 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 the 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 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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