XGBoost

Jun 28, 2024

Federated XGBoost Made Practical and Productive with NVIDIA FLARE

XGBoost is a highly effective and scalable machine learning algorithm widely employed for regression, classification, and ranking tasks. Building on the...

6 MIN READ

A diagram representing multi-GPU training.

Sep 07, 2023

Unlocking Multi-GPU Model Training with Dask XGBoost

As data scientists, we often face the challenging task of training large models on huge datasets. One commonly used tool, XGBoost, is a robust and efficient...

11 MIN READ

Jun 22, 2023

Applying Federated Learning to Traditional Machine Learning Methods

In the era of big data and distributed computing, traditional approaches to machine learning (ML) face a significant challenge: how to train models...

3 MIN READ

Feb 08, 2023

Categorical Features in XGBoost Without Manual Encoding

XGBoost is a decision-tree–based, ensemble machine learning algorithm based on gradient boosting. However, until recently, it didn’t natively support...

5 MIN READ

Oct 13, 2022

Upcoming Workshop: Applications of AI for Anomaly Detection

Learn to detect data abnormalities before they impact your business by using XGBoost, autoencoders, and GANs. Workshops are available in both the NALA and EMEA...

1 MIN READ

Oct 05, 2022

Explain Your Machine Learning Model Predictions with GPU-Accelerated SHAP

Machine learning (ML) is increasingly used across industries. Fraud detection, demand sensing, and credit underwriting are a few examples of specific use...

15 MIN READ

Feb 02, 2022

Real-time Serving for XGBoost, Scikit-Learn RandomForest, LightGBM, and More

The success of deep neural networks in multiple areas has prompted a great deal of thought and effort on how to deploy these models for use in real-world...

7 MIN READ

Jan 13, 2022

Accelerating Trustworthy AI for Credit Risk Management

On April 21, 2021, the EU Commission of the European Union issued a proposal for a regulation to harmonize the rules governing the design and marketing of AI...

13 MIN READ

Graphic with black background with DLI anomaly course name

Dec 15, 2021

NVIDIA DLI Teaches Supervised and Unsupervised Anomaly Detection

The NVIDIA Deep Learning Institute (DLI) is offering instructor-led, hands-on training on how to build applications of AI for anomaly detection. Anomaly...

5 MIN READ

Aug 02, 2021

Learn How to Build Applications of AI for Anomaly Detection

Whether you need to monitor cybersecurity threats, fraudulent financial transactions, product defects, or equipment health, artificial intelligence can help you...

2 MIN READ

Jun 17, 2021

Accelerating XGBoost on GPU Clusters with Dask

In XGBoost 1.0, we introduced a new official Dask interface to support efficient distributed training. Fast-forwarding to XGBoost 1.4, the interface is...

11 MIN READ

Jun 09, 2021

Advancing the State of the Art in AutoML, Now 10x Faster with NVIDIA GPUs and RAPIDS

To achieve state-of-the-art machine learning (ML) solutions, data scientists often build complex ML models. However, these techniques are computationally...

16 MIN READ

Apr 19, 2021

NASA and NVIDIA Collaborate to Accelerate Scientific Data Science Use Cases, Part 2

Over the past couple of years, NVIDIA and NASA have been working closely on accelerating data science workflows using RAPIDS, and integrating these...

6 MIN READ

Apr 08, 2021

NASA and NVIDIA Collaborate to Accelerate Scientific Data Science Use Cases, Part 1

Over the past couple of years, NVIDIA and NASA have been working closely on accelerating data science workflows using RAPIDS and integrating these...

7 MIN READ

Nov 12, 2020

Explaining and Accelerating Machine Learning for Loan Delinquencies

Machine learning (ML) can extract deep, complex insights out of data to help make decisions. In many cases, using more advanced models delivers real...

16 MIN READ

Feb 14, 2020

Learning to Rank with XGBoost and GPU

XGBoost is a widely used machine learning library, which uses gradient boosting techniques to incrementally build a better model during the training phase by...

13 MIN READ