XGBoost Reviews & Product Details

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. Review collected by and hosted on G2.com.

What I don't like about XGBoost is it doesn't handle the outliars in the dataset while machine learning model development. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

- 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.

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There is nothing much I dislike about the Xgboost but for me sometimes tuning the parameters is bit hectic. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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! Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

None I like everything about it and help me build faster understand and it’s good for programming Review collected by and hosted on G2.com.

XGBoost has better performance than other boosters or gradient functions. Helps return improved accuracy on regression algorithms. Works well on large datasets. Review collected by and hosted on G2.com.

Takes time to train on complex datasets. Requires cross validation for better results. Review collected by and hosted on G2.com.

it is helpful in building a model that is very accurate in fitting the training. Review collected by and hosted on G2.com.

it can be difficult to preempt overfitting the training data and generalize for testing. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

There was nothing that I disliked about it. Review collected by and hosted on G2.com.