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XGBoost

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13 reviews
  • 1 profiles
  • 1 categories
Average star rating
4.4
Serving customers since
2008

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XGBoost Reviews

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Verified User in Financial Services
GF
Verified User in Financial Services
01/24/2019
Validated Reviewer
Review source: G2 invite
Incentivized Review

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.
Ajay S.
AS
Ajay S.
Senior Software Engineer at fountain9(YC W21) | Co-Founder @Blubyn
01/24/2019
Validated Reviewer
Review source: G2 invite
Incentivized Review

One of the powerful Machine Learning algorithm

- 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.
Verified User in Building Materials
GB
Verified User in Building Materials
01/24/2019
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Review source: G2 invite
Incentivized Review

Awesome

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

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HQ Location:
San Francisco, US

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@github

What is XGBoost?

XGBoost (Extreme Gradient Boosting) is an open-source machine learning library that is widely recognized for its efficiency and performance. Designed for speed and performance, XGBoost is a scalable and flexible software framework that supports gradient boosting techniques. It is particularly popular due to its ability to handle large-scale and sparse data, making it a preferred choice for structured or tabular data across a range of classification and regression tasks.XGBoost provides a robust solution for data scientists aiming to achieve state-of-the-art results on predictive modeling challenges. It features several advanced capabilities such as handling missing values, tree pruning, and regularized boosting techniques which help to prevent overfitting and improve model performance.

Details

Year Founded
2008
Website
xgboost.ai