How to utilise XGBoost : xgbLinear model in R

Hits: 420

How to utilise XGBoost : xgbLinear model in R

XGBoost (eXtreme Gradient Boosting) is a powerful and widely-used machine learning algorithm, particularly in the field of gradient boosting. The xgbLinear model is a variation of XGBoost that is particularly well suited for linear problems. In R, the “xgboost” package can be used to build and use XGBoost models, including the xgbLinear model.

The first step in using an XGBoost model is to prepare your data. This includes cleaning and preprocessing the data, as well as splitting it into a training set and a test set.

Next, the “xgboost” function is used to build the model. The function takes several parameters such as the type of booster (xgbLinear), the objective(regression or classification) and any specific model tuning parameters.

Once the model is built, it can be used to make predictions on new, unseen data. It is important to keep in mind that XGBoost models are sensitive to small changes in the data, so it may be necessary to re-build and re-evaluate the model periodically.

XGBoost also have a built-in feature importance, which can be used to understand which features are important for making predictions.

In order to evaluate the model’s performance, a number of metrics can be used such as accuracy, precision and recall for classification tasks, and R-squared for regression tasks.

It’s worth mentioning that XGBoost is a powerful and efficient algorithm, but it does require a good understanding of the parameters and proper tuning in order to achieve the best performance.


In this Applied Machine Learning Recipe, you will learn: How to utilise XGBoost : xgbLinear model in R.

How to utilise XGBoost : xgbLinear model in R

Free Machine Learning & Data Science Coding Tutorials in Python & R for Beginners. Subscribe @ Western Australian Center for Applied Machine Learning & Data Science.

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $19.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!