Boosting Regression In R. In this tutorial well learn how to use the gbm model for regression in R. It supports various objective functions including regression classification.
Gradient boosting machines GBMs are an extremely popular machine learning algorithm that have proven successful across many domains and is one of the leading methods for winning Kaggle competitions. Algorithm learning consists of algorithm training within training data subset for optimal parameters estimation and algorithm testing within testing data subset using previously optimized parameters. This corresponds to a supervised regression machine learning task.
Aug 24 2017 Boosting is another famous ensemble learning technique in which we are not concerned with reducing the variance of learners like in Bagging where our aim is to reduce the high variance of learners by averaging lots of models fitted on bootstrapped data samples generated with replacement from training data so as to avoid overfitting.
This corresponds to a supervised regression machine learning task. Implementing GBM in R allows for a nice selection of exploratory plots including parameter contribution and partial dependence plots which provide a visual representation of the effect across values of a feature. Boosting can be used for both classification and regression problems. Set_params params Set the parameters of this estimator.
