--- title: XGBoost Reviews meta_title: 'XGBoost Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 13 reviews by the users' company size, role or industry to find out how XGBoost works for a business like yours. aggregate_rating: rating_value: 4.4 review_count: 13 scale: '5' date_modified: '2026-06-21' parent_category: name: Artificial Intelligence url: https://www.g2.com/categories/artificial-intelligence --- # XGBoost Reviews **Vendor:** XGBoost **Category:** [Machine Learning Software](https://www.g2.com/categories/machine-learning) **Average Rating:** 4.4/5.0 **Total Reviews:** 13 ## About XGBoost XGBoost is an optimized distributed gradient boosting library that is efficient, flexible and portable, it implements machine learning algorithms under the Gradient Boosting framework and provides a parallel tree boosting(also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. ## XGBoost Reviews ### 1. XGBoost for Machine learning models **Rating:** 4.5/5.0 stars **Reviewed by:** GOURI S. | Technical Lead Data Scientist, Mid-Market (51-1000 emp.) **Reviewed Date:** September 11, 2021 **What do you like best about XGBoost?** The best thing about XGBoost is it provides parallel processing in the machine learning model development; with the help of 4 cores and parallel processing, i was able to develop a machine learning model on 30 Million subscribers in 2 hours. **What do you dislike about XGBoost?** What I don't like about XGBoost is it doesn't handle the outliars in the dataset while machine learning model development. **What problems is XGBoost solving and how is that benefiting you?** I am using XGBoost for the machine learning model development on a large dataset. It is speedy and provides good results in terms of accuracy and other matrices. ### 2. The greatest boosting algorithm that existed so far **Rating:** 4.5/5.0 stars **Reviewed by:** Meliksah T. | Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** August 17, 2019 **What do you like best about XGBoost?** It's the best performing stand-alone algorithm (not counting deep learning algorithms which is whole another field) famous for winning many online machine learning competitions. It runs fast and performs better than bagging algorithms because it learns from the mistakes of previous tree models that were built within it. It is possible to tune XGBoost for various metrics, too so if you want a high recall, you can do it with the help of GridSearchCV. It is very efficient compared to famous Random Forest algorithm. **What do you dislike about XGBoost?** That it is not a part of a bigger package such as Anaconda but we have to install it separately. Also, its greatness comes with the cost of overfitting just like deep neural networks. It learns so good that after hyperparameter tuning it overfits more than other algorithms. **Recommendations to others considering XGBoost:** Be careful when hyperparameter tuning, it can overfit even though train and test data were separated. **What problems is XGBoost solving and how is that benefiting you?** I am solving Machine Learning problems with XGBoost. It learns and performs very well both in terms of performance metrics such as accuracy and fscore as well as training time. ### 3. Great algorithm to use for ML training **Rating:** 5.0/5.0 stars **Reviewed by:** Chathuri J. | University Undergaduate, Small-Business (50 or fewer emp.) **Reviewed Date:** January 20, 2019 **What do you like best about XGBoost?** I have used XGBoost models for many ML competition problems so far. Every time I could achieve a high accuracy and high performance model through using XGBoost. XGBoost is well known for its better performance and efficient memory management in ML community. Therefore, I highly recommend anyone who is new to the field to learn and use XGBoost. It is must to be in your ML toolkit. **What do you dislike about XGBoost?** The underlying concept of the algorithm is somewhat hard to understand at first. And the model has a large number of hyperparameters. Hence, at the beginning, it is difficult to understand the role each hyperparameter plays. But after some reading of the theory of the algorithm etc. the model becomes easy to comprehend and use. **Recommendations to others considering XGBoost:** Take some time to understand and its concept. Then, it becomes very easy to use XGBoost. **What problems is XGBoost solving and how is that benefiting you?** I used XGBoost to train models for financial data mostly. But, apart from business problems, I could use the models in ML and data science competitions, I recently participated. Each and every instance, I could achieve high prediction performances from XGBoost. The ability to give high accuracy models is one of the main advantages in XGBoost. Apart from it, it is fast in execution and easy to use. Hence, I believe this algorithm is one of the best tools a data scientist or a ML engineer should possess. ### 4. One of the powerful Machine Learning algorithm **Rating:** 4.5/5.0 stars **Reviewed by:** Ajay S. | Senior Software Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** January 24, 2019 **What do you like best about XGBoost?** - XgBoost is a type of library which you can install on your machine. C++, Java, Python with Sci-kit learn and many more. - It does parallelization tree construction using all CPU cores - The implementation of the algorithm was engineered for the efficiency of computing time and memory resources. - Xgboost ensures the execution speed and model performance - XGBoost internally has parameters for cross-validation, regularization, user-defined objective functions, missing values etc - It helps to reduce overfitting. **What do you dislike about XGBoost?** There is nothing much I dislike about the Xgboost but for me sometimes tuning the parameters is bit hectic. **Recommendations to others considering XGBoost:** The main reasons to use XgBoost is its execution speed and increase in model performance. You can solve the classification, regression and ranking problem very easily. There are lot of parameters and user define functions which will surely come in handy. It is always better to try XgBoost first. **What problems is XGBoost solving and how is that benefiting you?** If you don't know what to do use XgBoost algorithm. I will suggest using XgBoost according to your requirements. I use XgBoost over light GBM because it gives me better results. Mostly in Kaggle competitions, I use such algorithms. ### 5. Solid framework for gradient boosting in Python **Rating:** 3.5/5.0 stars **Reviewed by:** Verified User in Research | Mid-Market (51-1000 emp.) **Reviewed Date:** September 13, 2018 **What do you like best about XGBoost?** Have used XGBoost multiple times, and it is a very intuitive library that is easy to pick up quickly for the task I had at hand (fairly straightforward gradient boosting task). I only used the package in R form, but have heard good things from colleagues who much more regularly use gradient boosting for predictive projects; XGBoost seems to be the go-to library for boosting for multiple Data Scientists that I work with. **What do you dislike about XGBoost?** Nothing comes to mind; it is an efficient and easy to use gradient boosting framework. The support for the R version seems a little less than the Python version, but the R version performed well for my needs (relatively small dataset, no multicore processing or need for intense parallelization. **What problems is XGBoost solving and how is that benefiting you?** We leverage machine learning platforms and statistical methods to evaluate promising social interventions, pulling from many different fields and frameworks. XGBoost is a good implementation of a key technique in machine learning, and it was relatively simple to implement in a public policy context. ### 6. XGBoost **Rating:** 3.0/5.0 stars **Reviewed by:** Verified User in Financial Services | Enterprise (> 1000 emp.) **Reviewed Date:** January 24, 2019 **What do you like best about XGBoost?** The application is an easy to use, out-of-the-box software to quickly apply to data prediction problems. It is reliable and fast and portable, making it a versatile tool for machine learning. **What do you dislike about XGBoost?** There's not much to dislike. It's been pretty popular as a decision tree algorithm and rightly remains a reliable choice for data science applications. Only wished it was developed sooner! **What problems is XGBoost solving and how is that benefiting you?** Quickly developing a prediction process for data analysis. It has become one of the easiest algorithms to use for quickly prototyping a model. ### 7. Awesome **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Building Materials | Small-Business (50 or fewer emp.) **Reviewed Date:** January 24, 2019 **What do you like best about XGBoost?** The boost is your program makes a better stronger built it makes it easier to build it makes your computer access and easy to use and build your program **What do you dislike about XGBoost?** None I like everything about it and help me build faster understand and it’s good for programming **What problems is XGBoost solving and how is that benefiting you?** It solves problems by making your website business faster protect you from anything that like viruses or anything that will affect your business to help it grow ### 8. Fast, accurate and efficient library for machine learning **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Computer Software | Enterprise (> 1000 emp.) **Reviewed Date:** January 25, 2019 **What do you like best about XGBoost?** XGBoost has better performance than other boosters or gradient functions. Helps return improved accuracy on regression algorithms. Works well on large datasets. **What do you dislike about XGBoost?** Takes time to train on complex datasets. Requires cross validation for better results. **Recommendations to others considering XGBoost:** Use XGBoost for scalable applications and predictive analytics. When used appropriately, it delivers better results than contemporaries. **What problems is XGBoost solving and how is that benefiting you?** Prediction, Analytics, Data mining ### 9. ML algorithm good for accuracy **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Marketing and Advertising | Enterprise (> 1000 emp.) **Reviewed Date:** January 22, 2019 **What do you like best about XGBoost?** it is helpful in building a model that is very accurate in fitting the training. **What do you dislike about XGBoost?** it can be difficult to preempt overfitting the training data and generalize for testing. **What problems is XGBoost solving and how is that benefiting you?** marketing research of audience demographics and predicting behavior ### 10. Was great for boosting data **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Small-Business (50 or fewer emp.) **Reviewed Date:** July 26, 2018 **What do you like best about XGBoost?** I liked that it was very user friendly and incorporated data in a nice method. I liked the way it worked and it was easy to learn. Their staff was very good at assisting me throughout the process. Any questions that I had were answered immediately and without hesitation. They were kind and flexible to work with. I would definitely recommend. **What do you dislike about XGBoost?** There was nothing that I disliked about it. **Recommendations to others considering XGBoost:** I feel that this is a very good method if you are looking to gather all of your data together in a user friendly method and one that can be helpful to your organization. **What problems is XGBoost solving and how is that benefiting you?** Data and pulling everything together. ### 11. XGB is the best out of the box model available today **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Consumer Goods | Mid-Market (51-1000 emp.) **Reviewed Date:** July 26, 2018 **What do you like best about XGBoost?** Runs well in basically every situation, handles missing data well, relatively lightweight. **What do you dislike about XGBoost?** It's annoying that you need graphviz to easily visualize the tree output. I can't believe it'd be that hard to output it even as a csv or something. **What problems is XGBoost solving and how is that benefiting you?** We use XGB to predict store sales based on demographics, human traffic data, etc. XGB has produced significant improvements in terms of accuracy and flexibility over the previous GLM. ### 12. Gradient Boosting Library For Multiple Languages **Rating:** 5.0/5.0 stars **Reviewed by:** Tejasvini V. | Software Engineer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** October 25, 2017 **What do you like best about XGBoost?** - It's portability as it supports Windows, Linux, OS X and also some cloud platforms. - Multiple language support like: Python, R, Java and C++. - Detailed documentation. - There are various example in the documentation which really helps. **What do you dislike about XGBoost?** To be frank, using this library with python till now didn't face any problems. Don't know for other languages but works well with python. **Recommendations to others considering XGBoost:** If you are starting to learn it as well as a programming language too, start with python. It has great support for python and you will easily find tutorials. **What problems is XGBoost solving and how is that benefiting you?** - Building Machine Learning based models. - Developing projects and products that require Machine Learning algorithms as core. ### 13. A lot of documentation and great for ML competitions **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Higher Education | Small-Business (50 or fewer emp.) **Reviewed Date:** November 10, 2017 **What do you like best about XGBoost?** The documentation makes it really easy to get started after the install. There are plenty of examples online to learn from and is widely regarded in the data community. I have only used xgboost for classification in a Kaggle competition and it made me interested in gradient boosted techniques. **What do you dislike about XGBoost?** Directions for installing the package were not clear from what I remember. It can also be slow when training on a lot of data. LightGBM is another option for gradient boosting that trains much faster. **What problems is XGBoost solving and how is that benefiting you?** I have only used it for classification problems. I first used it in a Kaggle competition. Being able to try it out on my own and then see how other people on the discussion boards used xgboost really helped me learn how to use it. - [View XGBoost pricing details and edition comparison](https://www.g2.com/products/xgboost/reviews?section=pricing&secure%5Bexpires_at%5D=2026-06-25+08%3A34%3A59+-0500&secure%5Bsession_id%5D=468a5c14-6b1a-4717-83c9-2c34a60aac4c&secure%5Btoken%5D=fa44b9c89b9df4f76aa64c96457008dae5886e5a2352bf5511aad5ed1dbb3bc0&format=llm_user) ## XGBoost Features **Integration - Machine Learning** - Integration **Learning - Machine Learning** - Training Data - Actionable Insights - Algorithm ## Top XGBoost Alternatives - [Weka](https://www.g2.com/products/weka/reviews) - 4.3/5.0 (13 reviews) - [Google Cloud TPU](https://www.g2.com/products/google-cloud-tpu/reviews) - 4.5/5.0 (33 reviews) - [scikit-learn](https://www.g2.com/products/scikit-learn/reviews) - 4.8/5.0 (60 reviews)